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. |
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. |
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 . |
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 |
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. |
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). |
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. |
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 . |
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. |
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 . |
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. |
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 . |
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 . |
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). |