Index of papers in Proc. ACL that mention
  • parse tree
Cai, Jingsheng and Utiyama, Masao and Sumita, Eiichiro and Zhang, Yujie
Dependency-based Pre-ordering Rule Set
Figure 1 shows a constituent parse tree and its Stanford typed dependency parse tree for the same
Dependency-based Pre-ordering Rule Set
As shown in the figure, the number of nodes in the dependency parse tree (i.e.
Dependency-based Pre-ordering Rule Set
9) is much fewer than that in its corresponding constituent parse tree (i.e.
Introduction
These pre-ordering approaches first parse the source language sentences to create parse trees .
Introduction
Then, syntactic reordering rules are applied to these parse trees with the goal of reordering the source language sentences into the word order of the target language.
Introduction
terrorism definition (a) A constituent parse tree
parse tree is mentioned in 16 sentences in this paper.
Topics mentioned in this paper:
Tan, Ming and Zhou, Wenli and Zheng, Lei and Wang, Shaojun
Composite language model
Figure 1: A composite n-gram/m-SLM/PLSA language model where the hidden information is the parse tree T and semantic content 9.
Training algorithm
the lth sentence Wl with its parse tree structure Tl
Training algorithm
of tag 75 predicted by word 21) and the tags of m most recent exposed headwords in parse tree Tl of the lth sentence Wl in document d, and finally #(ahjn, Wl, Tl, d) is the count of constructor move a conditioning on m exposed headwords bin in parse tree Tl of the lth sentence Wl in document d.
Training algorithm
For a given sentence, its parse tree and semantic content are hidden and the number of parse trees grows faster than exponential with sentence length, Wang et al.
parse tree is mentioned in 14 sentences in this paper.
Topics mentioned in this paper:
Li, Zhenghua and Zhang, Min and Chen, Wenliang
Abstract
Instead of only using 1-best parse trees in previous work, our core idea is to utilize parse forest (ambiguous labelings) to combine multiple l-best parse trees generated from diverse parsers on unlabeled data.
Abstract
1) ambiguity encoded in parse forests compromises noise in l-best parse trees .
Abstract
During training, the parser is aware of these ambiguous structures, and has the flexibility to distribute probability mass to its preferred parse trees as long as the likelihood improves.
Introduction
Both work employs two parsers to process the unlabeled data, and only select as extra training data sentences on which the 1-best parse trees of the two parsers are identical.
Introduction
Different from traditional self/co/tri-training which only use l-best parse trees on unlabeled data, our approach adopts ambiguous labelings, represented by parse forest, as gold-standard for unlabeled sentences.
Introduction
The forest is formed by two parse trees , respectively shown at the upper and lower sides of the sentence.
parse tree is mentioned in 24 sentences in this paper.
Topics mentioned in this paper:
Zhao, Bing and Lee, Young-Suk and Luo, Xiaoqiang and Li, Liu
Abstract
We propose a novel technique of learning how to transform the source parse trees to improve the translation qualities of syntax-based translation models using synchronous context-free grammars.
Decoding
Given a grammar G, and the input source parse tree 7r from a monolingual parser, we first construct the elementary tree for a source span, and then retrieve all the relevant subgraphs seen in the given grammar through the proposed operators.
Elementary Trees to String Grammar
We propose to use variations of an elementary tree, which is a connected sub graph fitted in the original monolingual parse tree .
Elementary Trees to String Grammar
where of is a set of frontier nodes which contain nonterminals or words; of are the interior nodes with source la-bels/symbols; E is the set of edges connecting the nodes 12 = of +vi into a connected subgraph fitted in the source parse tree ; 6 is the immediate common parent of the frontier nodes of .
Experiments
There are 16 thousand human parse trees with human alignment; additional 1 thousand human parse and aligned sent-pairs are used as unseen test set to verify our MaxEnt models and parsers.
Introduction
For instance, in Arabic—to—English translation, we find only 45.5% of Arabic NP-SBJ structures are mapped to the English NP-SBJ with machine alignment and parse trees, and only 60.1% of NP-SBJs are mapped with human alignment and parse trees as in § 2.
Introduction
Mi and Huang (2008) introduced parse forests to blur the chunking decisions to a certain degree, to expand search space and reduce parsing errors from l-best trees (Mi et al., 2008); others tried to use the parse trees as soft constraints on top of unlabeled grammar such as Hiero (Marton and Resnik, 2008; Chiang, 2010; Huang et al., 2010; Shen et al., 2010) without sufficiently leveraging rich tree context.
Introduction
On the basis of our study on investigating the language divergence between Arabic-English with human aligned and parsed data, we integrate several simple statistical operations, to transform parse trees adaptively to serve the
The Projectable Structures
We carried out a controlled study on the projectable structures using human annotated parse trees and word alignment for 5k Arabic—English sentence-pairs.
The Projectable Structures
In Table 1, the unlabeled F-measures with machine alignment and parse trees show that, for only 48.71% of the time, the boundaries introduced by the source parses
The Projectable Structures
Table 1: The labeled and unlabeled F-measures for projecting the source nodes onto the target side via alignments and parse trees ; unlabeled F—measures show the bracketing accuracies for translating a source span contiguously.
parse tree is mentioned in 15 sentences in this paper.
Topics mentioned in this paper:
Wang, WenTing and Su, Jian and Tan, Chew Lim
Abstract
In this paper we propose using tree kernel based approach to automatically mine the syntactic information from the parse trees for discourse analysis, applying kernel function to the tree structures directly.
Incorporating Structural Syntactic Information
A parse tree that covers both discourse arguments could provide us much syntactic information related to the pair.
Incorporating Structural Syntactic Information
Both the syntactic flat path connecting connective and arguments and the 2-level production rules in the parse tree used in previous study can be directly described by the tree structure.
Incorporating Structural Syntactic Information
To present their syntactic properties and relations in a single tree structure, we construct a syntax tree for each paragraph by attaching the parsing trees of all its sentences to an upper paragraph node.
Introduction
Nevertheless, Ben and James (2007) only uses flat syntactic path connecting connective and arguments in the parse tree .
Introduction
(2009) uses 2-level production rules to represent parse tree information.
Introduction
information from the parse trees for discourse analysis, applying kernel function to the parse tree structures directly.
Related Work
While the feature based approach may not be able to fully utilize the syntactic information in a parse tree , an alternative to the feature-based methods, tree kernel methods (Haussler, 1999) have been proposed to implicitly explore features in a high dimensional space by employing a kernel function to calculate the similarity between two objects directly.
The Recognition Framework
One advantage of SVM is that we can use tree kernel approach to capture syntactic parse tree information in a particular high-dimension space.
parse tree is mentioned in 25 sentences in this paper.
Topics mentioned in this paper:
Shindo, Hiroyuki and Miyao, Yusuke and Fujino, Akinori and Nagata, Masaaki
Background and Related Work
Our SR-TSG work is built upon recent work on Bayesian TSG induction from parse trees (Post and Gildea, 2009; Cohn et al., 2010).
Background and Related Work
A derivation is a process of forming a parse tree .
Background and Related Work
Figure la shows an example parse tree and Figure lb shows its example TSG derivation.
Inference
We use Markov Chain Monte Carlo (MCMC) sampling to infer the SR-TSG derivations from parse trees .
Inference
We first infer latent symbol subcategories for every symbol in the parse trees , and then infer latent substitution sites stepwise.
Inference
After that, we unfiX that assumption and infer latent substitution sites given symbol-refined parse trees .
Symbol-Refined Tree Substitution Grammars
As with previous work on TSG induction, our task is the induction of SR-TSG derivations from a corpus of parse trees in an unsupervised fashion.
Symbol-Refined Tree Substitution Grammars
That is, we wish to infer the symbol subcategories of every node and substitution site (i.e., nodes where substitution occurs) from parse trees .
parse tree is mentioned in 16 sentences in this paper.
Topics mentioned in this paper:
Sun, Jun and Zhang, Min and Tan, Chew Lim
Abstract
Our study reveals that the structural features embedded in a bilingual parse tree pair are very effective for subtree alignment and the bilingual tree kernels can well capture such features.
Abstract
The experimental results show that our approach achieves a significant improvement on both gold standard tree bank and automatically parsed tree pairs against a heuristic similarity based method.
Introduction
A subtree alignment process pairs up subtree pairs across bilingual parse trees whose contexts are semantically translational equivalent.
Introduction
(2007), a subtree aligned parse tree pair follows the following criteria:
Introduction
Each pair consists of both the lexical constituents and their maximum tree structures generated over the lexical sequences in the original parse trees .
Substructure Spaces for BTKs
The plain syntactic structural features can deal with the structural divergence of bilingual parse trees in a more general perspective.
Substructure Spaces for BTKs
_ lin(S)| lin(T)I $161) _ lin(S)I lin(T)I S and T refer to the entire source and target parse trees respectively.
Substructure Spaces for BTKs
Therefore, |in(S)| and |in(T)| are the respective span length of the parse tree used for normalization.
parse tree is mentioned in 16 sentences in this paper.
Topics mentioned in this paper:
Yang, Nan and Li, Mu and Zhang, Dongdong and Yu, Nenghai
Abstract
In this work, we further extend this line of exploration and propose a novel but simple approach, which utilizes a ranking model based on word order precedence in the target language to reposition nodes in the syntactic parse tree of a source sentence.
Experiments
None means the original sentences without reordering; Oracle means the best permutation allowed by the source parse tree ; ManR refers to manual reorder rules; Rank means ranking reordering model.
Experiments
On the other hand, the performance of the ranking reorder model still fall far short of oracle, which is the lowest crossing-link number of all possible permutations allowed by the parse tree .
Introduction
The most notable solution to this problem is adopting syntaX-based SMT models, especially methods making use of source side syntactic parse trees .
Introduction
One is tree-to-string model (Quirk et al., 2005; Liu et al., 2006) which directly uses source parse trees to derive a large set of translation rules and associated model parameters.
Introduction
The other is called syntax pre-reordering — an approach that re-positions source words to approximate target language word order as much as possible based on the features from source syntactic parse trees .
Word Reordering as Syntax Tree Node Ranking
Given a source side parse tree T6, the task of word reordering is to transform Te to T4, so that 6’ can match the word order in target language as much as possible.
Word Reordering as Syntax Tree Node Ranking
By permuting tree nodes in the parse tree , the source sentence is reordered into the target language order.
Word Reordering as Syntax Tree Node Ranking
parse tree , we can obtain the same word order of Japanese translation.
parse tree is mentioned in 12 sentences in this paper.
Topics mentioned in this paper:
Zhang, Hui and Zhang, Min and Li, Haizhou and Aw, Aiti and Tan, Chew Lim
Abstract
Therefore, it can not only utilize forest structure that compactly encodes exponential number of parse trees but also capture non-syntactic translation equivalences with linguistically structured information through tree sequence.
Forest-based tree sequence to string model
parse trees ) for a given sentence under a context free grammar (CFG).
Forest-based tree sequence to string model
The two parse trees T1 and T2 encoded in Fig.
Forest-based tree sequence to string model
Different parse tree represents different derivations and explanations for a given sentence.
Introduction
In theory, one may worry about whether the advantage of tree sequence has already been covered by forest because forest encodes implicitly a huge number of parse trees and these parse trees may generate many different phrases and structure segmentations given a source sentence.
Related work
Here, a tree sequence refers to a sequence of consecutive sub-trees that are embedded in a full parse tree .
Related work
parse trees .
parse tree is mentioned in 18 sentences in this paper.
Topics mentioned in this paper:
Xiong, Deyi and Zhang, Min and Aw, Aiti and Li, Haizhou
Analysis
This proportion, which we call consistent constituent matching (CCM) rate , reflects the extent to which the translation output respects the source parse tree .
Experiments
We removed 15,250 sentences, for which the Chinese parser failed to produce syntactic parse trees .
Introduction
Consider the following Chinese fragment with its parse tree:
Introduction
However, the parse tree of the source fragment constrains the phrase “ER “13”” to be translated as a unit.
Introduction
Without considering syntactic constraints from the parse tree , the decoder makes wrong decisions not only on phrase movement but also on the lexical selection for the multi-meaning word “75’”.
The Acquisition of Bracketing Instances
Let c and e be the source sentence and the target sentence, W be the word alignment between them, T be the parse tree of c. We define a binary bracketing instance as a tuple (b,7'(cinj),7'(Cj+1nk),7'(cink)> where b E {bracketable,unbracketable}, cinj and cj+1nlc are two neighboring source phrases and 7'(T, 3) (7(3) for short) is a subtree function which returns the minimal subtree covering the source sequence 3 from the source parse tree T. Note that 7(cz-nk) includes both 7(cz-nj) and flog-+1.19).
The Acquisition of Bracketing Instances
1: Input: sentence pair (0, e), the parse tree T of c and the word alignment W between c and e 2: QR :2 (Z) 3: for each (i,j, k) E cdo 4: if There exist a target phrase can” aligned to Cinj and ep,,q aligned to Cj+1,_k; then
The Syntax-Driven Bracketing Model 3.1 The Model
These features capture syntactic “horizontal context” which demonstrates the expansion trend of the source phrase 3, 31 and 32 on the parse tree .
The Syntax-Driven Bracketing Model 3.1 The Model
The tree path 0(31)..0(3) connecting 0(31) and 0(3), 0(32)..0(3) connecting 0(32) and 0(3), and 0(3)..p connecting 0(3) and the root node p of the whole parse tree are used as features.
The Syntax-Driven Bracketing Model 3.1 The Model
These features provide syntactic “vertical context” which shows the generation history of the source phrases on the parse tree .
parse tree is mentioned in 14 sentences in this paper.
Topics mentioned in this paper:
Kim, Joohyun and Mooney, Raymond
Abstract
successful task completion) can be used as an alternative, experimentally demonstrating that its performance is comparable to training on gold-standard parse trees .
Background
They also altered the processes for constructing productions and mapping parse trees to MRs in order to make the construction of semantic interpretations more compositional and allow the efficient construction of more complex representa-
Background
A simplified version of a sample parse tree for Kim and Mooney’s model is shown in Figure 2.
Introduction
parse trees ) to train the discriminative classifier.
Modified Reranking Algorithm
Therefore, we modify it to rerank the parse trees generated by Kim and Mooney (2012)’s model.
Modified Reranking Algorithm
The approach requires three subcomponents: l) a GEN function that returns the list of top n candidate parse trees for each NL sentence produced by the generative model, 2) a feature function (I) that maps a NL sentence, 6, and a parse tree, y, into a real-valued feature vector (19(6, 3/) 6 Rd, and 3) a reference parse tree that is compared to the highest-scoring parse tree during training.
Modified Reranking Algorithm
However, grounded language learning tasks, such as our navigation task, do not provide reference parse trees for training examples.
parse tree is mentioned in 29 sentences in this paper.
Topics mentioned in this paper:
Manshadi, Mehdi and Li, Xiao
A grammar for semantic tagging
T wo equivalent CFSG parse trees
A grammar for semantic tagging
Figure (7a) shows an example of a parse tree generated for the query “Canon vs Sony Camera” in which B, Q, and T are abbreviations for Brand, Query, and Type, and U is a special tag for the words that does not fall into any other tag categories and have been left unlabeled in our corpus such as a, the, for, etc.
A grammar for semantic tagging
A more careful look at the grammar shows that there is another parse tree for this query as shown in figure (7b).
Abstract
In order to take contextual information into account, a discriminative model is used on top of the parser to re—rank the n—best parse trees generated by the parser.
Introduction
To overcome this limitation, we further present a discriminative re-ranking module on top of the parser to re-rank the n-best parse trees generated by the parser using contextual features.
Our Grammar Model
A CFSG parse tree
Our Grammar Model
A CFSG parse tree
Our Grammar Model
(9) EjP(Ai—> xi) = 1 Consider a sentence wle...wn, a parse tree T of this sentence, and an interior node v in T labeled with Av and assume that v1, 122, ...vk are the children of the node v in T. We define:
Parsing Algorithm
This information is necessary for the termination step in order to print the parse trees .
parse tree is mentioned in 14 sentences in this paper.
Topics mentioned in this paper:
Abend, Omri and Reichart, Roi and Rappoport, Ari
Algorithm
As preprocessing, we use an unsupervised parser that generates an unlabeled parse tree for each sen-
Algorithm
Second, they should be k-th degree cousins of the predicate in the parse tree .
Algorithm
Our algorithm attempts to find sub-trees within the parse tree , whose structure resembles the structure of a full sentence.
Experimental Setup
A minimal clause is the lowest ancestor of the verb in the parse tree that has a syntactic label of a clause according to the gold standard parse of the PTB.
Introduction
Initially, the set of possible arguments for a given verb consists of all the constituents in the parse tree that do not contain that predicate.
Introduction
Using this information, it further reduces the possible arguments only to those contained in the minimal clause, and further prunes them according to their position in the parse tree .
Related Work
In addition, most models assume that a syntactic representation of the sentence is given, commonly in the form of a parse tree , a dependency structure or a shallow parse.
Results
In our algorithm, the initial set of potential arguments consists of constituents in the Seginer parser’s parse tree .
parse tree is mentioned in 11 sentences in this paper.
Topics mentioned in this paper:
Lee, John and Seneff, Stephanie
Abstract
A basic approach is template matching on parse trees .
Abstract
To improve recall, irregularities in parse trees caused by verb form errors are taken into account; to improve precision, n-gram counts are utilized to filter proposed corrections.
Data 5.1 Development Data
To investigate irregularities in parse tree patterns (see §3.2), we utilized the AQUAINT Corpus of English News Text.
Introduction
We build on the basic approach of template-matching on parse trees in two ways.
Introduction
To improve recall, irregularities in parse trees caused by verb form errors are considered; to improve precision, n-gram counts are utilized to filter proposed corrections.
Previous Research
Similar strategies with parse trees are pursued in (Bender et al., 2004), and error templates are utilized in (Heidom, 2000) for a word processor.
Previous Research
Relative to verb forms, errors in these categories do not “disturb” the parse tree as much.
Research Issues
The success of this strategy, then, hinges on accurate identification of these items, for example, from parse trees .
Research Issues
In other words, sentences containing verb form errors are more likely to yield an “incorrect” parse tree , sometimes with significant differences.
Research Issues
One goal of this paper is to recognize irregularities in parse trees caused by verb form errors, in order to increase recall.
parse tree is mentioned in 14 sentences in this paper.
Topics mentioned in this paper:
Mi, Haitao and Huang, Liang and Liu, Qun
Abstract
Among syntax-based translation models, the tree-based approach, which takes as input a parse tree of the source sentence, is a promising direction being faster and simpler than its string-based counterpart.
Conclusion and future work
We have presented a novel forest-based translation approach which uses a packed forest rather than the 1-best parse tree (or k-best parse trees ) to direct the translation.
Experiments
Using more than one parse tree apparently improves the BLEU score, but at the cost of much slower decoding, since each of the top-k trees has to be decoded individually although they share many common subtrees.
Experiments
1' (rank of the parse tree picked by the decoder)
Experiments
Figure 5: Percentage of the i-th best parse tree being picked in decoding.
Forest-based translation
Informally, a packed parse forest, or forest in short, is a compact representation of all the derivations (i.e., parse trees ) for a given sentence under a context-free grammar (Billot and Lang, 1989).
Forest-based translation
The parse tree for the preposition case is shown in Figure 2(b) as the l-best parse, while for the conjunction case, the two proper nouns (Basin and Shalong) are combined to form a coordinated NP
Forest-based translation
Shown in Figure 3(a), these two parse trees can be represented as a single forest by sharing common subtrees such as NPB0,1 and VPB3,6.
Introduction
Depending on the type of input, these efforts can be divided into two broad categories: the string-based systems whose input is a string to be simultaneously parsed and translated by a synchronous grammar (Wu, 1997; Chiang, 2005; Galley et al., 2006), and the tree-based systems whose input is already a parse tree to be directly converted into a target tree or string (Lin, 2004; Ding and Palmer, 2005; Quirk et al., 2005; Liu et al., 2006; Huang et al., 2006).
Introduction
However, despite these advantages, current tree-based systems suffer from a major drawback: they only use the 1-best parse tree to direct the translation, which potentially introduces translation mistakes due to parsing errors (Quirk and Corston-Oliver, 2006).
parse tree is mentioned in 11 sentences in this paper.
Topics mentioned in this paper:
Gómez-Rodr'iguez, Carlos and Carroll, John and Weir, David
Dependency parsing schemata
Parse tree: A partial dependency tree 75 E D-trees is a parse tree for a given string wl .
Dependency parsing schemata
.Qn, we will say it is a projective parse tree for the string.
Dependency parsing schemata
Final items in this formalism will be those containing some forest F containing a parse tree for some arbitrary string.
Introduction
Each item contains a piece of information about the sentence’s structure, and a successful parsing process will produce at least one final item containing a full parse tree for the sentence or guaranteeing its existence.
Introduction
Items in parsing schemata are formally defined as sets of partial parse trees from a set denoted
Introduction
Trees(G), which is the set of all the possible partial parse trees that do not violate the constraints imposed by a grammar G. More formally, an item set I is defined by Sikkel as a quotient set associated with an equivalence relation on Trees(G).1
parse tree is mentioned in 13 sentences in this paper.
Topics mentioned in this paper:
Yogatama, Dani and Smith, Noah A.
Abstract
We introduce three linguistically motivated structured regularizers based on parse trees , topics, and hierarchical word clusters for text categorization.
Structured Regularizers for Text
Figure 1: An example of a parse tree from the Stanford sentiment treebank, which annotates sentiment at the level of every constituent (indicated here by —|— and ++; no marking indicates neutral sentiment).
Structured Regularizers for Text
4.2 Parse Tree Regularizer
Structured Regularizers for Text
Sentence boundaries are a rather superficial kind of linguistic structure; syntactic parse trees provide more fine-grained information.
parse tree is mentioned in 23 sentences in this paper.
Topics mentioned in this paper:
Zhang, Min and Jiang, Hongfei and Aw, Aiti and Li, Haizhou and Tan, Chew Lim and Li, Sheng
Introduction
1 A tree sequence refers to an ordered subtree sequence that covers a phrase or a consecutive tree fragment in a parse tree .
Related Work
Yamada and Knight (2001) use noisy-channel model to transfer a target parse tree into a source sentence.
Related Work
(2006) propose a feature-based discriminative model for target language syntactic structures prediction, given a source parse tree .
Related Work
(2006) create an xRS rule headed by a pseudo, non-syntactic nonterminal symbol that subsumes the phrase and its corresponding multi-headed syntactic structure; and one sibling xRS rule that explains how the pseudo symbol can be combined with other genuine non-terminals for acquiring the genuine parse trees .
Tree Sequence Alignment Model
source and target parse trees T ( fl‘]) and T (ell ) in Fig.
Tree Sequence Alignment Model
2 illustrates two examples of tree sequences derived from the two parse trees .
Tree Sequence Alignment Model
and their parse trees T(f1‘]) and T (611 ) 9 the tree
parse tree is mentioned in 18 sentences in this paper.
Topics mentioned in this paper:
Xiang, Bing and Luo, Xiaoqiang and Zhou, Bowen
Chinese Empty Category Prediction
For instance, Yang and Xue (2010) attempted to predict the existence of an EC before a word; Luo and Zhao (2011) predicted ECs on parse trees , but the position information of some ECs is partially lost in their representation.
Chinese Empty Category Prediction
Furthermore, Luo and Zhao (2011) conducted experiments on gold parse trees only.
Chinese Empty Category Prediction
our opinion, recovering ECs from machine parse trees is more meaningful since that is what one would encounter when developing a downstream application such as machine translation.
Integrating Empty Categories in Machine Translation
As mentioned in the previous section, the output of our EC predictor is a new parse tree with the labels and positions
Integrating Empty Categories in Machine Translation
In this work we also take advantages of the augmented Chinese parse trees (with ECs projected to the surface) and extract tree-to-string grammar (Liu et al., 2006) for a tree-to-string MT system.
Integrating Empty Categories in Machine Translation
Due to the recovered ECs in the source parse trees , the tree-to-string grammar extracted from such trees can be more discriminative, with an increased capability of distinguishing different context.
parse tree is mentioned in 25 sentences in this paper.
Topics mentioned in this paper:
Kaufmann, Tobias and Pfister, Beat
Abstract
We propose a language model based on a precise, linguistically motivated grammar (a handcrafted Head-driven Phrase Structure Grammar) and a statistical model estimating the probability of a parse tree .
Conclusions and Outlook
first step in this direction by estimating the probability of a parse tree .
Conclusions and Outlook
However, our model only looks at the structure of a parse tree and does not take the actual words into account.
Experiments
As P(T) does not directly apply to parse trees , all possible readings have to be unpacked.
Experiments
For these lattices the grammar-based language model was simply switched off in the experiment, as no parse trees were produced for efficiency reasons.
Language Model 2.1 The General Approach
(2) Pyram(W) is defined as the probability of the most likely parse tree of a word sequence W: P W = P T 3 gram( ) Tepggefiw) ( > ( ) To determine Pyram(W) is an expensive operation as it involves parsing.
Language Model 2.1 The General Approach
2.2 The Probability of a Parse Tree
Language Model 2.1 The General Approach
The parse trees produced by our parser are binary-branching and rather deep.
parse tree is mentioned in 10 sentences in this paper.
Topics mentioned in this paper:
Riesa, Jason and Marcu, Daniel
Features
Feature development Our features are inspired by analysis of patterns contained among our gold alignment data and automatically generated parse trees .
Features
link (e, f) if the part-of-speech tag of e is t. The conditional probabilities in this table are computed from our parse trees and the baseline Model 4 alignments.
Features
0 Features PP-NP-head, NP-DT-head, and VP-VP-head (Figure 6) all exploit headwords on the parse tree .
Introduction
Using a foreign string and an English parse tree as input, we formulate a bottom-up search on the parse tree , with the structure of the tree as a backbone for building a hypergraph of possible alignments.
Word Alignment as a Hypergraph
Algorithm input The input to our alignment algorithm is a sentence-pair (e’i‘, 1m) and a parse tree over one of the input sentences.
Word Alignment as a Hypergraph
To generate parse trees , we use the Berkeley parser (Petrov et al., 2006), and use Collins head rules (Collins, 2003) to head-out binarize each tree.
Word Alignment as a Hypergraph
Word alignments are built bottom-up on the parse tree .
parse tree is mentioned in 10 sentences in this paper.
Topics mentioned in this paper:
Ge, Ruifang and Mooney, Raymond
Abstract
Unlike previous methods, it exploits an existing syntactic parser to produce disam-biguated parse trees that drive the compositional semantic interpretation.
Ensuring Meaning Composition
3 only works if the syntactic parse tree strictly follows the predicate-argument structure of the MR, since meaning composition at each node is assumed to combine a predicate with one of its arguments.
Introduction
1Ge and Mooney (2005) use training examples with semantically annotated parse trees , and Zettlemoyer and Collins (2005) learn a probabilistic semantic parsing model
Introduction
This paper presents an approach to learning semantic parsers that uses parse trees from an existing syntactic analyzer to drive the interpretation process.
Learning Semantic Knowledge
Next, each resulting parse tree is linearized to produce a sequence of predicates by using a top-down, left-to-right traversal of the parse tree .
Semantic Parsing Framework
Th framework is composed of three components: 1 an existing syntactic parser to produce parse tree for NL sentences; 2) learned semantic knowledg
Semantic Parsing Framework
First, the syntactic parser produces a parse tree for the NL sentence.
Semantic Parsing Framework
3(a) shows one possible semantically-augmented parse tree (SAPT) (Ge and Mooney, 2005) for the condition part of the example in Fig.
parse tree is mentioned in 10 sentences in this paper.
Topics mentioned in this paper:
Martins, Andre and Smith, Noah and Xing, Eric
Dependency Parsing
Let us first describe formally the set of legal dependency parse trees .
Dependency Parsing
We define the set of legal dependency parse trees of at (denoted 34:10)) as the set of O-arborescences of D, i.e., we admit each arborescence as a potential dependency tree.
Dependency Parsing
Combinatorial algorithms (Chu and Liu, 1965; Edmonds, 1967) can solve this problem in cubic time.4 If the dependency parse trees are restricted to be projective, cubic-time algorithms are available via dynamic programming (Eisner, 1996).
Dependency Parsing as an ILP
Our formulations rely on a concise polyhedral representation of the set of candidate dependency parse trees , as sketched in §2.l.
Dependency Parsing as an ILP
For most languages, dependency parse trees tend to be nearly projective (cf.
Dependency Parsing as an ILP
It would be straightforward to adapt the constraints in §3.5 to allow only projective parse trees : simply force 23" = 0 for any a E A.
Experiments
net /projects /mstparser 11Note that, unlike reranking approaches, there are still exponentially many candidate parse trees after pruning.
Experiments
13Unlike our model, the hybrid models used here as baselines make use of the dependency labels at training time; indeed, the transition-based parser is trained to predict a labeled dependency parse tree , and the graph-based parser use these predicted labels as input features.
parse tree is mentioned in 9 sentences in this paper.
Topics mentioned in this paper:
Wang, Lu and Raghavan, Hema and Castelli, Vittorio and Florian, Radu and Cardie, Claire
Abstract
We present a sentence-compression-based framework for the task, and design a series of learning-based compression models built on parse trees .
Related Work
Rather than attempt to derive a new parse tree like Knight and Marcu (2000) and Galley and McKeown (2007), we learn to safely remove a set of constituents in our parse tree-based compression model while preserving grammatical structure and essential content.
Results
Those issues can be addressed by analyzing k-best parse trees and we leave it in the future work.
Sentence Compression
Our tree-based compression methods are in line with syntax-driven approaches (Galley and McKeown, 2007), where operations are carried out on parse tree constituents.
Sentence Compression
Unlike previous work (Knight and Marcu, 2000; Galley and McKeown, 2007), we do not produce a new parse tree,
Sentence Compression
Formally, given a parse tree T of the sentence to be compressed and a tree traversal algorithm, T can be presented as a list of ordered constituent nodes, T = 750751 .
parse tree is mentioned in 9 sentences in this paper.
Topics mentioned in this paper:
Parikh, Ankur P. and Cohen, Shay B. and Xing, Eric P.
Abstract
Cog-nitively, it is more plausible to assume that children obtain only terminal strings of parse trees and not the actual parse trees .
Abstract
Most existing solutions treat the problem of unsupervised parsing by assuming a generative process over parse trees e.g.
Abstract
Unlike in phylogenetics and graphical models, where a single latent tree is constructed for all the data, in our case, each part of speech sequence is associated with its own parse tree .
parse tree is mentioned in 9 sentences in this paper.
Topics mentioned in this paper:
Wang, Zhiguo and Xue, Nianwen
Joint POS Tagging and Parsing with Nonlocal Features
Assuming an input sentence contains n words, in order to reach a terminal state, the initial state requires n sh—x actions to consume all words in 6, and n — l rl/rr—x actions to construct a complete parse tree by consuming all the subtrees in 0.
Joint POS Tagging and Parsing with Nonlocal Features
For example, the parse tree in Figure la contains no ru—x action, while the parse tree for the same input sentence in Figure lb contains four ru—x actions.
Joint POS Tagging and Parsing with Nonlocal Features
Input: A word-segmented sentence, beam size k. Output: A constituent parse tree .
Transition-based Constituent Parsing
empty stack 0 and a queue 6 containing the entire input sentence (word-POS pairs), and the terminal states have an empty queue 6 and a stack 0 containing only one complete parse tree .
Transition-based Constituent Parsing
In order to construct lexicalized constituent parse trees , we define the following actions for the action set T according to (Sagae and Lavie, 2005; Wang et al., 2006; Zhang and Clark, 2009):
Transition-based Constituent Parsing
For example, in Figure l, for the input sentence wowlwg and its POS tags abc, our parser can construct two parse trees using action sequences given below these trees.
parse tree is mentioned in 9 sentences in this paper.
Topics mentioned in this paper:
Cohen, Shay B. and Johnson, Mark
Bayesian inference for PCFGs
In the supervised setting the data D consists of a corpus of parse trees D = (t1, .
Bayesian inference for PCFGs
Ignoring issues of tightness for the moment and setting P(t | 6)) = Me (If) , this means that in the supervised setting the posterior distribution P(@ | 13,04) given a set of parse trees 1: = (t1, .
Bayesian inference for PCFGs
The algorithms we give here are based on their Gibbs sampler, which in each iteration first samples parse trees
Introduction
Cognitively it is implausible that children can perceive the parse trees of the language they are learning, but it is more reasonable to assume that they can obtain the terminal strings or yield of these trees.
Introduction
(2007) proposed MCMC samplers for the posterior distribution over rule probabilities and the parse trees of the training data strings.
Introduction
timates are always tight for both the supervised case (where the input consists of parse trees ) and the unsupervised case (where the input consists of yields or terminal strings).
parse tree is mentioned in 8 sentences in this paper.
Topics mentioned in this paper:
Kalchbrenner, Nal and Grefenstette, Edward and Blunsom, Phil
Abstract
The network does not rely on a parse tree and is easily applicable to any language.
Background
A model that adopts a more general structure provided by an external parse tree is the Recursive Neural Network (RecNN) (Pollack, 1990; Kiichler and Goller, 1996; Socher et al., 2011; Hermann and Blunsom, 2013).
Background
It is sensitive to the order of the words in the sentence and it does not depend on external language-specific features such as dependency or constituency parse trees .
Experiments
RECNTN is a recursive neural network with a tensor-based feature function, which relies on external structural features given by a parse tree and performs best among the RecNNs.
Introduction
The feature graph induces a hierarchical structure somewhat akin to that in a syntactic parse tree .
Properties of the Sentence Model
The recursive neural network follows the structure of an external parse tree .
Properties of the Sentence Model
Likewise, the induced graph structure in a DCNN is more general than a parse tree in that it is not limited to syntactically dictated phrases; the graph structure can capture short or long-range semantic relations between words that do not necessarily correspond to the syntactic relations in a parse tree .
Properties of the Sentence Model
The DCNN has internal input-dependent structure and does not rely on externally provided parse trees , which makes the DCNN directly applicable to hard-to-parse sentences such as tweets and to sentences from any language.
parse tree is mentioned in 8 sentences in this paper.
Topics mentioned in this paper:
Finkel, Jenny Rose and Manning, Christopher D.
Base Models
Figure 3c shows a parse tree representation of a semi-CRF.
Base Models
Let t be a complete parse tree for sentence 3, and each local subtree 7“ E t encodes both the rule from the grammar, and the span and split information (e.g NP(7,9) —> JJ(7,8)NN(8,9) which covers the last two words in Figure l).
Base Models
f(7~,s)} (9) r675 To compute the partition function ZS, which serves to normalize the function, we must sum over 7(3), the set of all possible parse trees for sentence 3.
Experiments and Discussion
For the hierarchical model, we used the CNN portion of the data (5093 sentences) for the extra named entity data (and ignored the parse trees ) and the remaining portions combined for the extra parse data (and ignored the named entity annotations).
Introduction
When trained separately, these single-task models can produce outputs which are inconsistent with one another, such as named entities which do not correspond to any nodes in the parse tree (see Figure l for an example).
Introduction
Because a named entity should correspond to a node in the parse tree , strong evidence about either aspect of the model should positively impact the other aspect
parse tree is mentioned in 7 sentences in this paper.
Topics mentioned in this paper:
Li, Junhui and Marton, Yuval and Resnik, Philip and Daumé III, Hal
Experiments
To obtain syntactic parse trees and semantic roles on the tuning and test datasets, we first parse the source sentences with the Berkeley Parser (Petrov and Klein, 2007), trained on the Chinese Treebank 7.0 (Xue et al., 2005).
Experiments
In order to understand how well the MR08 system respects their reordering preference, we use the gold alignment dataset LDC2006E86, in which the source sentences are from the Chinese Treebank, and thus both the gold parse trees and gold predicate-argument structures are available.
Related Work
(2012) obtained word order by using a reranking approach to reposition nodes in syntactic parse trees .
Unified Linguistic Reordering Models
According to the annotation principles in (Chinese) PropB ank (Palmer et al., 2005; Xue and Palmer, 2009), all the roles in a PAS map to a corresponding constituent in the parse tree , and these constituents (e.g., NPs and VBD in Figure 1) do not overlap with each other.
Unified Linguistic Reordering Models
parse tree and its word alignment links to the target language.
Unified Linguistic Reordering Models
Given a hypothesis H with its alignment a, it traverses all CFG rules in the parse tree and sees if two adjacent constituents are conditioned to trigger the reordering models (lines 2-4).
parse tree is mentioned in 7 sentences in this paper.
Topics mentioned in this paper:
Chen, Xiao and Kit, Chunyu
Abstract
This paper presents a higher-order model for constituent parsing aimed at utilizing more local structural context to decide the score of a grammar rule instance in a parse tree .
Conclusion
This paper has presented a higher-order model for constituent parsing that factorizes a parse tree into larger parts than before, in hopes of increasing its power of discriminating the true parse from the others without losing tractability.
Higher-order Constituent Parsing
Figure l: A part of a parse tree centered at NP —> NP VP
Higher-order Constituent Parsing
A part in a parse tree is illustrated in Figure 1.
Introduction
Previous discriminative parsing models usually factor a parse tree into a set of parts.
Introduction
It allows multiple adjacent grammar rules in each part of a parse tree , so as to utilize more local structural context to decide the plausibility of a grammar rule instance.
parse tree is mentioned in 6 sentences in this paper.
Topics mentioned in this paper:
Hoyt, Frederick and Baldridge, Jason
Deriving Eisner Normal Form
(30) For a set S of semantically equivalent2 parse trees for a string ABC, admit the unique parse tree such that at least one of (i) or (ii) holds:
Deriving Eisner Normal Form
(31) Theorem 1 : For every parse tree oz, there is a semantically equivalent parse-tree N F(a) in which no node resulting from application of B or S functions as the primary functor in a rule application.
Deriving Eisner Normal Form
(32) Theorem 2: If N F(a) and N F(o/ ) are distinct parse trees , then their model-theoretic interpretations are distinct.
parse tree is mentioned in 6 sentences in this paper.
Topics mentioned in this paper:
Zhang, Yuan and Lei, Tao and Barzilay, Regina and Jaakkola, Tommi and Globerson, Amir
Experimental Setup
We split the sentence based on the ending punctuation, predict the parse tree for each segment and group the roots of resulting trees into a single node.
Introduction
Because the number of alternatives is small, the scoring function could in principle involve arbitrary (global) features of parse trees .
Related Work
Reranking can be combined with an arbitrary scoring function, and thus can easily incorporate global features over the entire parse tree .
Sampling-Based Dependency Parsing with Global Features
Ideally, we would change multiple heads in the parse tree simultaneously, and sample those choices from the corresponding conditional distribution of p. While in general this is increasingly difficult with more heads, it is indeed tractable if
Sampling-Based Dependency Parsing with Global Features
3/ is always a valid parse tree if we allow multiple children of the root and do not impose projective constraint.
Sampling-Based Dependency Parsing with Global Features
We extend our model such that it jointly learns how to predict a parse tree and also correct the predicted POS tags for a better parsing performance.
parse tree is mentioned in 6 sentences in this paper.
Topics mentioned in this paper:
Wang, Lu and Cardie, Claire
Content Selection
Both the indicator and argument take the form of constituents in the parse tree .
Surface Realization
It takes as input a set of relation instances (from the same cluster) R = {(indi, argi)}i]:1 that are produced by content selection component, a set of templates T = {tj that are represented as parsing trees , a transformation function F (described below), and a statistical ranker S for ranking the generated abstracts, for which we defer description later in this Section.
Surface Realization
The transformation function F models the constituent-level transformations of relation instances and their mappings to the parse trees of templates.
Surface Realization
F all-Constitnent Mapping denotes that a source constituent is mapped directly to a target constituent of the template parse tree with the same tag.
parse tree is mentioned in 6 sentences in this paper.
Topics mentioned in this paper:
Stern, Asher and Stern, Roni and Dagan, Ido and Felner, Ariel
Background
We focus on methods that perform transformations over parse trees , and highlight the search challenge with which they are faced.
Background
In our domain, each state is a parse tree , which is expanded by performing all applicable transformations.
Search for Textual Inference
Let t be a parse tree , and let 0 be a transformation.
Search for Textual Inference
Denoting by tT and tH the text parse tree and the hypothesis parse tree , a proof system has to find a sequence 0 with minimal cost such that tT lO m. This forms a search problem of finding the lowest-cost proof among all possible proofs.
Search for Textual Inference
Next, for a transformation 0, applied on a parse tree If, we define arequiredfi, 0) as the subset of 75’s nodes required for applying 0 (i.e., in the absence of these nodes, 0 could not be applied).
parse tree is mentioned in 6 sentences in this paper.
Topics mentioned in this paper:
Hermann, Karl Moritz and Blunsom, Phil
Experiments
We use the C&C parser (Clark and Curran, 2007) to generate CCG parse trees for the data used in our experiments.
Experiments
We assume fixed parse trees for all of the compounds (Figure 6), and use these to compute compound level vectors for all word pairs.
Experiments
Our experimental findings indicate a clear advantage for a deeper integration of syntax over models that use only the bracketing structure of the parse tree .
Introduction
We achieve this goal by employing the CCG formalism to consider compositional structures at any point in a parse tree .
Model
We use the parse tree to structure an RAE, so that each combinatory step is represented by an autoencoder function.
Model
As an internal baseline we use model CCAE-A, which is an RAE structured along a CCG parse tree .
parse tree is mentioned in 6 sentences in this paper.
Topics mentioned in this paper:
Hirao, Tsutomu and Suzuki, Jun and Isozaki, Hideki
Introduction
English sentences are usually analyzed by a full parser to make parse trees , and the trees are then trimmed (Knight and Marcu, 2002; Turner and Chamiak, 2005; Unno et al., 2006).
Introduction
For Japanese, dependency trees are trimmed instead of full parse trees (Takeuchi and Matsumoto, 2001; Oguro et al., 2002; Nomoto, 2008)1 This parsing approach is reasonable because the compressed output is grammatical if the
Related work
For instance, most English sentence compression methods make full parse trees and trim them by applying the generative model (Knight and Marcu, 2002; Turner and Charniak, 2005), discrimina-tive model (Knight and Marcu, 2002; Unno et a1., 2006).
Related work
For Japanese sentences, instead of using full parse trees , existing sentence compression methods trim dependency trees by the discrim-inative model (Takeuchi and Matsumoto, 2001; Nomoto, 2008) through the use of simple linear combined features (Oguro et a1., 2002).
Related work
They simply regard a sentence as a word sequence and structural information, such as full parse tree or dependency trees, are encoded in the sequence as features.
parse tree is mentioned in 6 sentences in this paper.
Topics mentioned in this paper:
Ganchev, Kuzman and Gillenwater, Jennifer and Taskar, Ben
Approach
(2005) also note these problems and solve them by introducing dozens of rules to transform the transferred parse trees .
Experiments
The baseline constructs a full parse tree from the incomplete and possibly conflicting transferred edges using a simple random process.
Introduction
In particular, we address challenges (1) and (2) by avoiding commitment to an entire projected parse tree in the target language during training.
Parsing Models
The parsing model defines a conditional distribution p9(z | x) over each projective parse tree 2 for a particular sentence X, parameterized by a vector 6.
Parsing Models
where z is a directed edge contained in the parse tree 2 and gb is a feature function.
Parsing Models
where r(x) is the part of speech tag of the root of the parse tree 2, z is an edge from parent zp to child 20 in direction zd, either left or right, and vz indicates valency—false if zp has no other children further from it in direction zd than 20, true otherwise.
parse tree is mentioned in 6 sentences in this paper.
Topics mentioned in this paper:
Zhai, Feifei and Zhang, Jiajun and Zhou, Yu and Zong, Chengqing
Experiment
To relieve the negative effect of SRL errors, we get the multiple SRL results by providing the SRL system with 3-best parse trees of Berkeley parser (Petrov and Klein, 2007), 1-best parse tree of Bikel parser (Bikel, 2004) and Stanford parser (Klein and Manning, 2003).
Experiment
Thus, the system using PASTRs can only attach the long phrase to the predicate “511:” according to the parse tree , and meanwhile, make use of a transformation rule as follows:
Inside Context Integration
The stag sequence dominates the corresponding syntactic tree fragments in the parse tree .
Inside Context Integration
(2012) attached the IC to its neighboring elements based on parse trees .
Maximum Entropy PAS Disambiguation (MEPD) Model
These features include st(Ei), i.e., the highest syntax tag for each argument, and fst(PAS) which is the lowest father node of Sp in the parse tree .
parse tree is mentioned in 5 sentences in this paper.
Topics mentioned in this paper:
Agirre, Eneko and Baldwin, Timothy and Martinez, David
Background
While a detailed description of the respective parsing models is beyond the scope of this paper, it is worth noting that both parsers induce a context free grammar as well as a generative parsing model from a training set of parse trees , and use a development set to tune internal parameters.
Experimental setting
One of the main requirements for our dataset is the availability of gold-standard sense and parse tree annotations.
Experimental setting
The gold-standard parse tree annotations are required in order to carry out evaluation of parser and PP attachment performance.
Experimental setting
Following Atterer and Schutze (2007), we wrote a script that, given a parse tree , identifies instances of PP attachment ambiguity and outputs the (v, n1 , p, n2) quadruple involved and the attachment decision.
Introduction
Traditionally, parse disambiguation has relied on structural features extracted from syntactic parse trees , and made only limited use of semantic information.
parse tree is mentioned in 5 sentences in this paper.
Topics mentioned in this paper:
Zhu, Xiaodan and Guo, Hongyu and Mohammad, Saif and Kiritchenko, Svetlana
Experiment setup
Data As described earlier, the Stanford Sentiment Treebank (Socher et al., 2013) has manually annotated, real-valued sentiment values for all phrases in parse trees .
Introduction
The recently available Stanford Sentiment Treebank (Socher et al., 2013) renders manually annotated, real-valued sentiment scores for all phrases in parse trees .
Related work
Such models work in a bottom-up fashion over the parse tree of a sentence to infer the sentiment label of the sentence as a composition of the sentiment expressed by its constituting parts.
Semantics-enriched modeling
A recursive neural tensor network (RNTN) is a specific form of feed-forward neural network based on syntactic (phrasal-structure) parse tree to conduct compositional sentiment analysis.
Semantics-enriched modeling
Each node of the parse tree is a fixed-length vector that encodes compositional semantics and syntax, which can be used to predict the sentiment of this node.
parse tree is mentioned in 5 sentences in this paper.
Topics mentioned in this paper:
Mylonakis, Markos and Sima'an, Khalil
Abstract
The key assumption behind many approaches is that translation is guided by the source and/or target language parse, employing rules extracted from the parse tree or performing tree transformations.
Conclusions
A further promising direction is broadening this set with labels taking advantage of both source and target-language linguistic annotation or categories exploring additional phrase-pair properties past the parse trees such as semantic annotations.
Experiments
The results in Table 2(a) indicate that a large part of the performance improvement can be attributed to the use of the linguistic annotations extracted from the source parse trees , indicating the potential of the LTS system to take advantage of such additional annotations to deliver better translations.
Introduction
Recent research tries to address these issues, by restructuring training data parse trees to better suit syntax-based SMT training (Wang et al., 2010), or by moving from linguistically motivated synchronous grammars to systems where linguistic plausibility of the translation is assessed through additional features in a phrase-based system (Venugopal et al., 2009; Chiang et al., 2009), obscuring the impact of higher level syntactic processes.
Related Work
Earlier approaches for linguistic syntax-based translation such as (Yamada and Knight, 2001; Galley et al., 2006; Huang et al., 2006; Liu et al., 2006) focus on memorising and reusing parts of the structure of the source and/or target parse trees and constraining decoding by the input parse tree .
parse tree is mentioned in 5 sentences in this paper.
Topics mentioned in this paper:
Zhang, Dongdong and Li, Mu and Duan, Nan and Li, Chi-Ho and Zhou, Ming
Experiments
might be incorrect due to errors in English parse trees .
Experiments
Given a source sentence, the corresponding syntax parse tree T S is first constructed with an English parser.
Experiments
The other problem comes from the English head word selection error introduced by using source parse trees .
Model Training and Application 3.1 Training
Based on the source syntax parse tree , for each measure word, we identified its head word by using a toolkit from (Chiang and Bikel, 2002) which can heuristically identify head words for sub-trees.
Our Method
The source head word feature is defined to be a function fl to indicate whether a word ei is the source head word in English according to a parse tree of the source sentence.
parse tree is mentioned in 5 sentences in this paper.
Topics mentioned in this paper:
Wu, Xianchao and Matsuzaki, Takuya and Tsujii, Jun'ichi
Fine-grained rule extraction
Considering that a parse tree is a trivial packed forest, we only use the term forest to expand our discussion, hereafter.
Introduction
Dealing with the parse error problem and rule sparseness problem, Mi and Huang (2008) replaced the l-best parse tree with a packed forest which compactly encodes exponentially many parses for tree-to-string rule extraction.
Related Work
fi] is a sentence of a foreign language other than English, E5 is a l-best parse tree of an English sentence E = e{, and A = {(j, is an alignment between the words in F and E.
Related Work
Considering the parse error problem in the l-best or k-best parse trees , Mi and Huang (2008) extracted tree-to-string translation rules from aligned packed forest-string pairs.
Related Work
In an HPSG parse tree , these lexical syntactic descriptions are included in the LEXENTRY feature (refer to Table 2) of a lexical node (Matsuzaki et al., 2007).
parse tree is mentioned in 5 sentences in this paper.
Topics mentioned in this paper:
Wan, Xiaojun and Li, Huiying and Xiao, Jianguo
Machine Translation Quality Prediction
We use the Stanford LeXicalized Parser (Klein and Manning, 2002) with the provided English PCFG model to parse a sentence into a parse tree .
Machine Translation Quality Prediction
1) Depth of the parse tree: It refers to the depth of the generated parse tree .
Machine Translation Quality Prediction
2) Number of SBARs in the parse tree : SBAR is defined as a clause introduced by a (possibly empty) subordinating conjunction.
parse tree is mentioned in 5 sentences in this paper.
Topics mentioned in this paper:
Nguyen, Minh Luan and Tsang, Ivor W. and Chai, Kian Ming A. and Chieu, Hai Leong
Experiments
The constituent parse trees were then transformed into dependency parse trees , using the head of each constituent (Jiang and Zhai, 2007b).
Problem Statement
We extract features from a sequence representation and a parse tree representation of each relation instance.
Problem Statement
Syntactic Features The syntactic parse tree of the relation instance sentence can be augmented to represent the relation instance.
Problem Statement
Each node in the sequence or the parse tree is augmented by an argument tag that indicates whether the node corresponds to entity A, B, both, or neither.
parse tree is mentioned in 5 sentences in this paper.
Topics mentioned in this paper:
Angeli, Gabor and Uszkoreit, Jakob
Learning
Inference A discriminative k-best parser was used to allow for arbitrary features in the parse tree .
Learning
Unlike syntactic parsing, child types of a parse tree uniquely define the parent type of the rule; this is a direct consequence of our combination rules being functions with domains defined in terms of the temporal types, and therefore necessarily projecting their inputs into a single output type.
Temporal Representation
The root of a parse tree should be one of these types.
Temporal Representation
At the root of a parse tree , we recursively apply
parse tree is mentioned in 4 sentences in this paper.
Topics mentioned in this paper:
Zhu, Muhua and Zhang, Yue and Chen, Wenliang and Zhang, Min and Zhu, Jingbo
Improved hypotheses comparison
Unlike dependency parsing, constituent parse trees for the same sentence can have different numbers of nodes, mainly due to the existence of unary nodes.
Improved hypotheses comparison
Figure 2: Example parse trees of the same sentence with different numbers of actions.
Introduction
The pioneering models rely on a classifier to make local decisions, and search greedily for a transition sequence to build a parse tree .
Introduction
One difference between phrase-structure parsing and dependency parsing is that for the former, parse trees with different numbers of unary rules require different numbers of actions to build.
parse tree is mentioned in 4 sentences in this paper.
Topics mentioned in this paper:
Vickrey, David and Koller, Daphne
Introduction
In the sentence “He expected to receive a prize for winning,” the path from “win” to its ARGO, “he”, involves the verbs “expect” and “receive” and the preposition “for.” The corresponding path through the parse tree likely occurs a relatively small number of times (or not at all) in the training corpus.
Simple Sentence Production
This procedure is quite expensive; we have to copy the entire parse tree at each step, and in general, this procedure could generate an exponential number of transformed parses.
Simplification Data Structure
In our case, the AND nodes are similar to constituent nodes in a parse tree — each has a category (e.g.
Transformation Rules
A transformation rule takes as input a parse tree and produces as output a different, changed parse tree .
parse tree is mentioned in 4 sentences in this paper.
Topics mentioned in this paper:
Yang, Bishan and Cardie, Claire
Discussion
Recall that the joint model finds the global optimal solution over a set of opinion entity and relation candidates, which are obtained from the n-best CRF predictions and constituents in the parse tree that satisfy certain syntactic patterns.
Model
Phrase type: the syntactic category of the deepest constituent that covers the candidate in the parse tree , e.g.
Model
2We use the Stanford Parser to generate parse trees and dependency graphs.
Model
Neighboring constituents: The words and grammatical roles of neighboring constituents of the opinion expression in the parse tree — the left and right sibling of the deepest constituent containing the opinion expression in the parse tree .
parse tree is mentioned in 4 sentences in this paper.
Topics mentioned in this paper:
duVerle, David and Prendinger, Helmut
Building a Discourse Parser
The algorithm starts with a list of all atomic discourse sub-trees (made of single edus in their text order) and recursively selects the best match between adjacent sub-trees (using binary classifier S), labels the newly created subtree (using multi-label classifier L) and updates scoring for S, until only one subtree is left: the complete rhetorical parse tree for the input text.
Evaluation
In each case, parse trees are evaluated using the four following, increasingly complex, matching criteria: blank tree structure (‘S’), tree structure with nuclearity (‘N’), tree structure with rhetorical relations (‘R’) and our final goal: fully labeled structure with both nuclearity and rhetorical relation labels (‘F’).
Features
A promising concept introduced by Soricut and Marcu (2003) in their sentence-level parser is the identification of ‘dominance sets’ in the syntax parse trees associated to each input sentence.
Introduction
To the best of our knowledge, Reitter’s (2003b) was the only previous research based exclusively on feature-rich supervised learning to produce text-level RST discourse parse trees .
parse tree is mentioned in 4 sentences in this paper.
Topics mentioned in this paper:
Jiang, Jing
Task definition
Following our preVious work (J iang and Zhai, 2007b), we extract features from a sequence representation and a parse tree representation of each relation instance.
Task definition
Each node in the sequence or the parse tree is augmented by an argument tag that indicates whether the node subsumes arg-I, arg-2, both or neither.
Task definition
(2008), we trim the parse tree of a relation instance so that it contains only the most essential components.
parse tree is mentioned in 4 sentences in this paper.
Topics mentioned in this paper:
Gyawali, Bikash and Gardent, Claire
Generating from the KBGen Knowledge-Base
To extract a Feature-Based Lexicalised Tree Adjoining Grammar (FB-LTAG) from the KB Gen data, we parse the sentences of the training corpus; project the entity and event variables to the syntactic projection of the strings they are aligned with; and extract the elementary trees of the resulting FB-LTAG from the parse tree using semantic information.
Generating from the KBGen Knowledge-Base
After alignment, the entity and event variables occurring in the input semantics are associated with substrings of the yield of the syntactic parse tree .
Generating from the KBGen Knowledge-Base
Once entity and event variables have been projected up the parse trees , we extract elementary FB-LTAG trees and their semantics from the input scenario as follows.
parse tree is mentioned in 4 sentences in this paper.
Topics mentioned in this paper:
Sun, Jun and Zhang, Min and Tan, Chew Lim
Introduction
(2006) statistically report that discontinuities are very useful for translational equivalence analysis using binary branching structures under word alignment and parse tree constraints.
NonContiguous Tree sequence Align-ment-based Model
Figure 2: A word-aligned parse tree pair
NonContiguous Tree sequence Align-ment-based Model
Given the source and target sentence f1] and e{, as well as the corresponding parse trees T(f1]) and T(e{), our approach directly approximates the posterior probability Pr(T(e{)|T(f1])) based on the log-linear framework:
Tree Sequence Pair Extraction
(2006) also reports that allowing gaps in one side only is enough to eliminate the hierarchical alignment failure with word alignment and one side parse tree constraints.
parse tree is mentioned in 4 sentences in this paper.
Topics mentioned in this paper:
Abney, Steven and Bird, Steven
Human Language Project
It is also notoriously difficult to obtain agreement about how parse trees should be defined in one language, much less in many languages simultaneously.
Human Language Project
Let us suppose that the purpose of a parse tree is to mediate interpretation.
Human Language Project
sus on parse trees is difficult, obtaining consensus on meaning representations is impossible.
parse tree is mentioned in 4 sentences in this paper.
Topics mentioned in this paper:
Chan, Yee Seng and Roth, Dan
Mention Extraction System
We extract the label of the parse tree constituent (if it exists) that exactly covers the mention, and also labels of all constituents that covers the mention.
Mention Extraction System
From a sentence, we gather the following as candidate mentions: all nouns and possessive pronouns, all named entities annotated by the the NE tagger (Ratinov and Roth, 2009), all base noun phrase (NP) chunks, all chunks satisfying the pattern: NP (PP NP)+, all NP constituents in the syntactic parse tree , and from each of these constituents, all substrings consisting of two or more words, provided the sub-strings do not start nor end on punctuation marks.
Syntactico-Semantic Structures
with lw of m,- in the sentence Syntactic parse-label of parse tree constituent parse that exactly covers m,-
Syntactico-Semantic Structures
parse-labels of parse tree constituents covering 771,;
parse tree is mentioned in 4 sentences in this paper.
Topics mentioned in this paper:
Jiang, Wenbin and Liu, Qun
Projected Classification Instance
Suppose a bilingual sentence pair, composed of a source sentence e and its target translation f. ye is the parse tree of the source sentence.
Projected Classification Instance
We define a boolean-valued function 6 (y, i, j, 7“) to investigate the dependency relationship of word 2' and word j in parse tree y:
Word-Pair Classification Model
Ideally, given the classification results for all candidate word pairs, the dependency parse tree can be composed of the candidate edges with higher score (1 for the boolean-valued classifier, and large p for the real-valued classifier).
Word-Pair Classification Model
This strategy alleviate the classification errors to some degree and ensure a valid, complete dependency parsing tree .
parse tree is mentioned in 4 sentences in this paper.
Topics mentioned in this paper:
Mi, Haitao and Liu, Qun
Introduction
By incorporating the syntactic annotations of parse trees from both or either side(s) of the bitext, they are believed better than phrase-based counterparts in reorderings.
Introduction
Depending on the type of input, these models can be broadly divided into two categories (see Table l): the string-based systems whose input is a string to be simultaneously parsed and translated by a synchronous grammar, and the tree-based systems whose input is already a parse tree to be directly converted into a target tree or string.
Model
A constituency forest (in Figure 1 left) is a compact representation of all the derivations (i.e., parse trees ) for a given sentence under a context-free grammar (Billot and Lang, 1989).
Model
The solid line in Figure 1 shows the best parse tree , while the dashed one shows the second best tree.
parse tree is mentioned in 4 sentences in this paper.
Topics mentioned in this paper:
Lee, Kenton and Artzi, Yoav and Dodge, Jesse and Zettlemoyer, Luke
Parsing Time Expressions
Figure l: A CCG parse tree for the mention “one week ago.” The tree includes forward (>) and backward (<) application, as well as two type-shifting operations
Parsing Time Expressions
The lexicon pairs words with categories and the combinators define how to combine categories to create complete parse trees .
Parsing Time Expressions
For example, Figure 1 shows a CCG parse tree for the phrase “one week ago.” The parse tree is read top to bottom, starting from assigning categories to words using the lexicon.
Resolution
Model Let y be a context-dependent CCG parse, which includes a parse tree TR(y), a set of context operations CNTX(y) applied to the logical form at the root of the tree, a final context-dependent logical form LF(y) and a TIMEX3 value Define gb(m, D, 3/) 6 Rd to be a d-dimensional feature—vector representation and 6 6 Rd to be a parameter vector.
parse tree is mentioned in 4 sentences in this paper.
Topics mentioned in this paper:
Zollmann, Andreas and Vogel, Stephan
Conclusion and discussion
with the model of Zollmann and Venugopal (2006), using heuristically generated labels from parse trees .
Introduction
(2006), target language parse trees are used to identify rules and label their nonterminal symbols, while Liu et al.
Introduction
(2006) use source language parse trees instead.
Introduction
Zollmann and Venugopal (2006) directly extend the rule extraction procedure from Chiang (2005) to heuristically label any phrase pair based on target language parse trees .
parse tree is mentioned in 4 sentences in this paper.
Topics mentioned in this paper:
Wu, Fei and Weld, Daniel S.
Experiments
Most likely, this is because TextRunner’s heuristics rely on parse trees to label training examples,
Experiments
The Stanford Parser is used to derive dependencies from CJ50 and gold parse trees .
Related Work
Deep features are derived from parse trees with the hope of training better extractors (Zhang et al., 2006; Zhao and Grishman, 2005; Bunescu and Mooney, 2005; Wang, 2008).
Wikipedia-based Open IE
Third, it discards the sentence if the subject and the attribute value do not appear in the same clause (or in parent/child clauses) in the parse tree .
parse tree is mentioned in 4 sentences in this paper.
Topics mentioned in this paper:
Narayan, Shashi and Gardent, Claire
Abstract
First, it is semantic based in that it takes as input a deep semantic representation rather than e.g., a sentence or a parse tree .
Introduction
While previous simplification approaches starts from either the input sentence or its parse tree , our model takes as input a deep semantic representation namely, the Discourse Representation Structure (DRS, (Kamp, 1981)) assigned by Boxer (Curran et al., 2007) to the input complex sentence.
Related Work
Their simplification model encodes the probabilities for four rewriting operations on the parse tree of an input sentences namely, substitution, reordering, splitting and deletion.
Related Work
(2010) and the edit history of Simple Wikipedia, Woodsend and Lapata (2011) learn a quasi synchronous grammar (Smith and Eisner, 2006) describing a loose alignment between parse trees of complex and of simple sentences.
parse tree is mentioned in 4 sentences in this paper.
Topics mentioned in this paper:
Bodenstab, Nathan and Dunlop, Aaron and Hall, Keith and Roark, Brian
Background
We define an edge’s figure-of-merit (FOM) as an estimate of the product of its inside (6) and outside (04) scores, conceptually the relative merit the edge has to participate in the final parse tree (see Figure 1).
Background
predictions about the unlabeled constituent structure of the target parse tree .
Beam-Width Prediction
The optimal point will necessarily be very conservative, allowing outliers (sentences or sub-phrases with above average ambiguity) to stay within the beam and produce valid parse trees .
Introduction
Exhaustive search for the maximum likelihood parse tree with a state-of-the-art grammar can require over a minute of processing for a single sentence of 25 words, an unacceptable amount of time for real-time applications or when processing millions of sentences.
parse tree is mentioned in 4 sentences in this paper.
Topics mentioned in this paper:
Pighin, Daniele and Cornolti, Marco and Alfonseca, Enrique and Filippova, Katja
Heuristics-based pattern extraction
The input to the algorithm are a parse tree T and a set of target entities E. We first generate combinations of 1-3 elements of E (line 10), then for each combination 0 we identify all the nodes in T that mention any of the entities in C. We continue by constructing the MST of these nodes, and finally apply our heuristics to the nodes in the MST.
Memory-based pattern extraction
We highlighted in bold the path corresponding to the linearized form (b) of the example parse tree (a).
Pattern extraction by sentence compression
Instead, we chose to modify the method of Filippova and Altun (2013) because it relies on dependency parse trees and does not use any LM scoring.
parse tree is mentioned in 3 sentences in this paper.
Topics mentioned in this paper:
Hermann, Karl Moritz and Blunsom, Phil
Approach
Most prior work on learning compositional semantic representations employs parse trees on their training data to structure their composition functions (Socher et al., 2012; Hermann and Blunsom, 2013, inter alia).
Approach
While these methods have been shown to work in some cases, the need for parse trees and annotated data limits such approaches to resource-fortunate languages.
Overview
This removes a number of constraints that normally come with CVM models, such as the need for syntactic parse trees , word alignment or annotated data as a training signal.
parse tree is mentioned in 3 sentences in this paper.
Topics mentioned in this paper:
Iyyer, Mohit and Enns, Peter and Boyd-Graber, Jordan and Resnik, Philip
Introduction
Figure 1: An example of compositionality in ideological bias detection (red —> conservative, blue —> liberal, gray —> neutral) in which modifier phrases and punctuation cause polarity switches at higher levels of the parse tree .
Recursive Neural Networks
Based on a parse tree , these words form phrases p (Figure 2).
Where Compositionality Helps Detect Ideological Bias
The increased accuracy suggests that the trained RNNs are capable of detecting bias polarity switches at higher levels in parse trees .
parse tree is mentioned in 3 sentences in this paper.
Topics mentioned in this paper:
Wang, Chang and Fan, James
Background
Many of them focus on using tree kernels to learn parse tree structure related features (Collins and Duffy, 2001; Culotta and Sorensen, 2004; Bunescu and Mooney, 2005).
Identifying Key Medical Relations
Figure 2: A Parse Tree Example
Identifying Key Medical Relations
Consider the sentence: “Antibiotics are the standard therapy for Lyme disease”: MedicalESG first generates a dependency parse tree (Figure 2) to represent grammatical relations between the words in the sentence, and then associates the words with CUIs.
parse tree is mentioned in 3 sentences in this paper.
Topics mentioned in this paper:
Guzmán, Francisco and Joty, Shafiq and Màrquez, Llu'is and Nakov, Preslav
Abstract
We first design two discourse-aware similarity measures, which use all-subtree kernels to compare discourse parse trees in accordance with the Rhetorical Structure Theory.
Conclusions and Future Work
First, we defined two simple discourse-aware similarity metrics (lexicalized and un-lexicalized), which use the all-subtree kernel to compute similarity between discourse parse trees in accordance with the Rhetorical Structure Theory.
Experimental Setup
Combination of four metrics based on syntactic information from constituency and dependency parse trees : ‘CP—STM-4’, ‘DP-HWCM_c-4’, ‘DP—HWCM1-4’, and ‘DP-Or(*)’.
parse tree is mentioned in 3 sentences in this paper.
Topics mentioned in this paper:
Lin, Chen and Miller, Timothy and Kho, Alvin and Bethard, Steven and Dligach, Dmitriy and Pradhan, Sameer and Savova, Guergana
Background
Figure 2: A parse tree (left) and its descending paths according to Definition 1 (l - length).
Methods
Definition 1 (Descending Path): Let T be a parse tree , 2) any nonterminal node in T, do a descendant of 2), including terminals.
Methods
Figure 2 illustrates a parse tree and its descending paths of different lengths.
parse tree is mentioned in 3 sentences in this paper.
Topics mentioned in this paper:
Wu, Xianchao and Matsuzaki, Takuya and Tsujii, Jun'ichi
Abstract
Therefore, the proposed approach can not only capture source-tree-to-target-chunk correspondences but can also use forest structures that compactly encode an exponential number of parse trees to properly generate target function words during decoding.
Backgrounds
3The forest includes three parse trees rooted at CD, cl, and c2.
Composed Rule Extraction
0 C(21): the complement span of U, which is the union of corresponding spans of nodes 21’ that share an identical parse tree with 2) but are neither antecedents nor descendants of v;
parse tree is mentioned in 3 sentences in this paper.
Topics mentioned in this paper:
Berg-Kirkpatrick, Taylor and Gillick, Dan and Klein, Dan
Efficient Prediction
Variables 20 indicate edges in the parse tree that have been cut in order to remove subtrees.
Joint Model
Variables yn indicate the presence of parse tree nodes.
Joint Model
We represent a compressive summary as a vector y = (yn : n E 258, s E c) of indicators, one for each nonterminal node in each parse tree of the sentences in the document set c. A word is present in the output summary if and only if its parent parse tree node n has yn = 1 (see Figure lb).
parse tree is mentioned in 3 sentences in this paper.
Topics mentioned in this paper:
Finkel, Jenny Rose and Kleeman, Alex and Manning, Christopher D.
Experiments
In Figure 3 we show for an example from section 22 the parse trees produced by our generative model and our feature-based discriminative model, and the correct parse.
The Model
of the parse tree , given the sentence, not joint likelihood of the tree and sentence; and (b) probabilities are normalized globally instead of locally —the graphical models depiction of our trees is undirected.
The Model
We define t"(s) to be the set of all possible parse trees for the given sentence licensed by the grammar G.
parse tree is mentioned in 3 sentences in this paper.
Topics mentioned in this paper:
Goldberg, Yoav and Tsarfaty, Reut
A Generative PCFG Model
212,, and a morphological analyzer, we look for the most probable parse tree 7r s.t.
A Generative PCFG Model
Hence, our parser searches for a parse tree 7r over lexemes (ll H.119) s.t.
A Generative PCFG Model
Thus our proposed model is a proper model assigning probability mass to all (7r, L) pairs, where 7r is a parse tree and L is the one and only lattice that a sequence of characters (and spaces) W over our alpha-beth gives rise to.
parse tree is mentioned in 3 sentences in this paper.
Topics mentioned in this paper:
Schulte im Walde, Sabine and Hying, Christian and Scheible, Christian and Schmid, Helmut
Summary and Outlook
Furthermore, we aim to use the verb class model in NLP tasks, (i) as resource for lexical induction of verb senses, verb alternations, and collocations, and (ii) as a lexical resource for the statistical disambiguation of parse trees .
Verb Class Model 2.1 Probabilistic Model
Figure 1: Example parse tree .
Verb Class Model 2.1 Probabilistic Model
(b) The training tuples are processed: For each tuple, a PCFG parse forest as indicated by Figure l is done, and the Inside-Outside algorithm is applied to estimate the frequencies of the ”parse tree rules”, given the current model probabilities.
parse tree is mentioned in 3 sentences in this paper.
Topics mentioned in this paper:
Zhao, Shiqi and Wang, Haifeng and Liu, Ting and Li, Sheng
Proposed Method
Let SE be an English sentence, TE the parse tree of SE, 6 a word of SE, we define the subtree and partial subtree following the definitions in (Ouan-graoua et al., 2007).
Proposed Method
If e,-is a descendant of ej in the parse tree , we remove p05,- from PE(e).
Proposed Method
Note that the Chinese patterns are not extracted from parse trees .
parse tree is mentioned in 3 sentences in this paper.
Topics mentioned in this paper:
Abend, Omri and Rappoport, Ari
Algorithm
A sequence of words will be marked as an argument of the verb if it is a constituent that does not contain the verb (according to the unsupervised parse tree ), whose parent is an ancestor of the verb.
Algorithm
Each word in the argument is now represented by its word form (without lemmatization), its unsupervised POS tag and its depth in the parse tree of the argument.
Algorithm
Instead, only those appearing in the top level (depth = l) of the argument under its unsupervised parse tree are taken.
parse tree is mentioned in 3 sentences in this paper.
Topics mentioned in this paper:
Huang, Fei and Yates, Alexander
Introduction
We then gradually add in less-sparse alternatives for the syntactic features that previous systems derive from parse trees .
Introduction
In standard SRL systems, these path features usually consist of a sequence of constituent parse nodes representing the shortest path through the parse tree between a word and the predicate (Gildea and Jurafsky, 2002).
Introduction
We substitute paths that do not depend on parse trees .
parse tree is mentioned in 3 sentences in this paper.
Topics mentioned in this paper:
Liu, Shujie and Li, Chi-Ho and Zhou, Ming
Evaluation
The major drawback of PER is that not all decisions in pruning would impact on alignment quality, since certain F-spans are of little use to the entire ITG parse tree .
Pruning in ITG Parsing
Once the complete parse tree is built, the k-best list of the topmost span is obtained by minimally expanding the list of alignment hypotheses of minimal number of span pairs.
The DPDI Framework
If the sentence-level annotation satisfies the alignment constraints of ITG, then each F-span will have only one E-span in the parse tree .
parse tree is mentioned in 3 sentences in this paper.
Topics mentioned in this paper:
Tomanek, Katrin and Hahn, Udo and Lohmann, Steffen and Ziegler, Jürgen
Cognitively Grounded Cost Modeling
As for syntactic complexity, we use two measures based on structural complexity including (a) the number of nodes of a constituency parse tree which are dominated by the annotation phrase (cf.
Experimental Design
We defined two measures for the complexity of the annotation examples: The syntactic complexity was given by the number of nodes in the constituent parse tree which are dominated by the annotation phrase (Szmrecsanyi, 2004).1 According to a threshold on the number of nodes in such a parse tree , we classified CNPs as having either high or low syntactic complexity.
Introduction
Structural complexity emerges, e. g., from the static topology of phrase structure trees and procedural graph traversals exploiting the topology of parse trees (see Szmrecsanyi (2004) or Cheung and Kemper (1992) for a survey of metrics of this type).
parse tree is mentioned in 3 sentences in this paper.
Topics mentioned in this paper:
Abu-Jbara, Amjad and Radev, Dragomir
Approach
Figure 1 shows a portion of the parse tree for Sentence (1) (from Section 1).
Approach
We extract the scope of the reference from the parse tree as follows.
Approach
For example, the parse tree shown in Figure 1 suggests that the scope of the reference is:
parse tree is mentioned in 3 sentences in this paper.
Topics mentioned in this paper:
Zhang, Meishan and Zhang, Yue and Che, Wanxiang and Liu, Ting
Character-based Chinese Parsing
As shown in Figure 5, a state ST consists of a stack S and a queue Q, where S = -- ,81,SO) contains partially constructed parse trees , and Q = (Q07Q1,°°° 7Qn—j) = (Cj,Cj+1,°°° ,Cn) iS th€ sequence of input characters that have not been processed.
Character-based Chinese Parsing
parse tree 80 must correspond to a fullword
Introduction
With richer information than word-level trees, this form of parse trees can be useful for all the aforementioned Chinese NLP applications.
parse tree is mentioned in 3 sentences in this paper.
Topics mentioned in this paper:
Zhang, Hao and Fang, Licheng and Xu, Peng and Wu, Xiaoyun
Forest-to-string Translation
The search problem is finding the derivation with the highest probability in the space of all derivations for all parse trees for an input sentence.
Introduction
Second, the parse tree is restructured using our binarization algorithm, resulting in a binary packed forest.
Source Tree Binarization
In a correct English parse tree , however, the subject-verb boundary is between “There” and “is”.
parse tree is mentioned in 3 sentences in this paper.
Topics mentioned in this paper:
Constant, Matthieu and Sigogne, Anthony and Watrin, Patrick
Evaluation
In order to compare both approaches, parse trees generated by BKYc were automatically transformed in trees with the same MWE annotation scheme as the trees generated by BKY.
MWE-dedicated Features
The reranker templates are instantiated only for the nodes of the candidate parse tree , which are leaves dominated by a MWE node (i.e.
MWE-dedicated Features
dominated by a MWE node m in the current parse tree p,
parse tree is mentioned in 3 sentences in this paper.
Topics mentioned in this paper:
Feng, Vanessa Wei and Hirst, Graeme
Method
Since EDU boundaries are highly correlated with the syntactic structures embedded in the sentences, EDU segmentation is a relatively trivial step — using machine- generated syntactic parse trees , HILDA achieves an F -score of 93.8% for EDU segmentation.
Method
HILDA’s features: We incorporate the original features used in the HILDA discourse parser with slight modification, which include the following four types of features occurring in SL, SR, or both: (1) N-gram prefixes and suffixes; (2) syntactic tag prefixes and suffixes; (3) lexical heads in the constituent parse tree ; and (4) PCS tag of the dominating nodes.
Related work
They showed that the production rules extracted from constituent parse trees are the most effective features, while contextual features are the weakest.
parse tree is mentioned in 3 sentences in this paper.
Topics mentioned in this paper:
Takamatsu, Shingo and Sato, Issei and Nakagawa, Hiroshi
Experiments
We used syntactic features (i.e., features obtained from the dependency parse tree of a sentence) and lexical features, and entity types, which essentially correspond to the ones developed by Mintz et a1.
Knowledge-based Distant Supervision
Since two entities mentioned in a sentence do not always have a relation, we select entity pairs from a corpus when: (i) the path of the dependency parse tree between the corresponding two named entities in the sentence is no longer than 4 and (ii) the path does not contain a sentence-like boundary, such as a relative clause1 (Banko et al., 2007; Banko and Etzioni, 2008).
Wrong Label Reduction
We define a pattern as the entity types of an entity pair2 as well as the sequence of words on the path of the dependency parse tree from the first entity to the second one.
parse tree is mentioned in 3 sentences in this paper.
Topics mentioned in this paper:
Yamangil, Elif and Shieber, Stuart
Inference
Following previous work, we design a blocked Metropolis-Hastings sampler that samples derivations per entire parse trees all at once in a joint fashion (Cohn and Blunsom, 2010; Shindo et al., 2011).
Introduction
Recent work that incorporated Dirichlet process (DP) nonparametric models into TSGs has provided an efficient solution to the problem of segmenting training data trees into elementary parse tree fragments to form the grammar (Cohn et al., 2009; Cohn and Blunsom, 2010; Post and Gildea, 2009).
Introduction
Figure 2: TIG-to-TSG transform: (a) and (b) illustrate transformed TSG derivations for two different TIG derivations of the same parse tree structure.
parse tree is mentioned in 3 sentences in this paper.
Topics mentioned in this paper:
Braune, Fabienne and Seemann, Nina and Quernheim, Daniel and Maletti, Andreas
Decoding
12Theoretically, this allows that the decoder ignores unary parser nonterminals, which could also disappear when we make our rules shallow; e. g., the parse tree left in the pre-translation of Figure 5 can be matched by a rule with left-hand side NP(Official, forecasts).
Theoretical Model
Since we utilize syntactic parse trees , let us introduce trees first.
Translation Model
8Actually, t must embed in the parse tree of 6; see Section 4.
parse tree is mentioned in 3 sentences in this paper.
Topics mentioned in this paper:
Liu, Yang
Introduction
While parsing algorithms can be used to parse partial translations in phrase-based decoding, the search space is significantly enlarged since there are exponentially many parse trees for exponentially many translations.
Introduction
They suffice to operate on well-formed structures and produce projective dependency parse trees .
Introduction
In addition, their algorithm produces phrasal dependency parse trees while the leaves of our dependency trees are words, making dependency language models can be directly used.
parse tree is mentioned in 3 sentences in this paper.
Topics mentioned in this paper:
Socher, Richard and Bauer, John and Manning, Christopher D. and Andrew Y., Ng
Introduction
The goal of supervised parsing is to learn a function 9 : 26 —> y, where X is the set of sentences and y is the set of all possible labeled binary parse trees .
Introduction
The loss increases the more incorrect the proposed parse tree is (Goodman, 1998).
Introduction
Assume, for now, we are given a labeled parse tree as shown in Fig.
parse tree is mentioned in 3 sentences in this paper.
Topics mentioned in this paper: