As preprocessing, we use an unsupervised parser that generates an unlabeled parse tree for each sen-

Second, they should be k-th degree cousins of the predicate in the parse tree .

Our algorithm attempts to find sub-trees within the parse tree , whose structure resembles the structure of a full sentence.

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.

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.

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 .

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.

In our algorithm, the initial set of potential arguments consists of constituents in the Seginer parser’s parse tree .

T wo equivalent CFSG parse trees

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 more careful look at the grammar shows that there is another parse tree for this query as shown in figure (7b).

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.

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.

A CFSG parse tree

A CFSG parse tree

(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:

This information is necessary for the termination step in order to print the parse trees .

This proportion, which we call consistent constituent matching (CCM) rate , reflects the extent to which the translation output respects the source parse tree .

We removed 15,250 sentences, for which the Chinese parser failed to produce syntactic parse trees .

Consider the following Chinese fragment with its parse tree:

However, the parse tree of the source fragment constrains the phrase “ER “13”” to be translated as a unit.

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’”.

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).

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

These features capture syntactic “horizontal context” which demonstrates the expansion trend of the source phrase 3, 31 and 32 on the parse tree .

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.

These features provide syntactic “vertical context” which shows the generation history of the source phrases on the parse tree .

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.

parse trees ) for a given sentence under a context free grammar (CFG).

The two parse trees T1 and T2 encoded in Fig.

Different parse tree represents different derivations and explanations for a given sentence.

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.

Here, a tree sequence refers to a sequence of consecutive sub-trees that are embedded in a full parse tree .

parse trees .

Unlike previous methods, it exploits an existing syntactic parser to produce disam-biguated parse trees that drive the compositional semantic interpretation.

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.

1Ge and Mooney (2005) use training examples with semantically annotated parse trees , and Zettlemoyer and Collins (2005) learn a probabilistic semantic parsing model

This paper presents an approach to learning semantic parsers that uses parse trees from an existing syntactic analyzer to drive the interpretation process.

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 .

Th framework is composed of three components: 1 an existing syntactic parser to produce parse tree for NL sentences; 2) learned semantic knowledg

First, the syntactic parser produces a parse tree for the NL sentence.

3(a) shows one possible semantically-augmented parse tree (SAPT) (Ge and Mooney, 2005) for the condition part of the example in Fig.

Let us first describe formally the set of legal dependency parse trees .

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.

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).

Our formulations rely on a concise polyhedral representation of the set of candidate dependency parse trees , as sketched in §2.l.

For most languages, dependency parse trees tend to be nearly projective (cf.

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.

net /projects /mstparser 11Note that, unlike reranking approaches, there are still exponentially many candidate parse trees after pruning.

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.

(2005) also note these problems and solve them by introducing dozens of rules to transform the transferred parse trees .

The baseline constructs a full parse tree from the incomplete and possibly conflicting transferred edges using a simple random process.

In particular, we address challenges (1) and (2) by avoiding commitment to an entire projected parse tree in the target language during training.

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.

where z is a directed edge contained in the parse tree 2 and gb is a feature function.

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.

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).

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

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).

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).

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.

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.

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’).

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.

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 .

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.

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.

(2008), we trim the parse tree of a relation instance so that it contains only the most essential components.

(2006) statistically report that discontinuities are very useful for translational equivalence analysis using binary branching structures under word alignment and parse tree constraints.

Figure 2: A word-aligned parse tree pair

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:

(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.