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.

1(a) according to the syntactic parse in Fig.

Macro-predicates are introduced as needed during training in order to ensure that each MR in the training set can be composed using the syntactic parse of its corresponding NL given reasonable assignments of predicates to words.

Previous methods for learning semantic parsers do not utilize an existing syntactic parser that provides disambiguated parse trees.1 However, accurate syntactic parsers are available for many

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

5), including a semantic lexicon to assign possible predicates (meanings) to words, and a set of semantic composition rules to construct possible MRs for each internal node in a syntactic parse given its children’s MRs; and 3) a statistical disambiguation model (cf.

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

As an alternative to syntactic parsing , we propose two novel features, intra-sentence positional term weighting (IPTW) and the patched language model (PLM) for our syntax-free sentence compressor.

Conventional sentence compression methods employ a syntactic parser to compress a sentence without changing its meaning.

As an alternative to syntactic parsing , we propose a novel term weighting technique based on the positional information within the original sentence and a novel language model that combines statistics from the original sentence and a general corpus.

Because our method does not use a syntactic parser , it is 4.3 times faster than Hori’s method.

In addition, sentence compression methods that strongly depend on syntactic parsers have two problems: ‘parse error’ and ‘decoding speed.’ 44% of sentences output by a state-of-the-art Japanese dependency parser contain at least one error (Kudo and Matsumoto, 2005).

It is significantly superior to the methods that employ syntactic parsers .

0 As an alternative to the syntactic parser , we proposed two novel features, Intra-sentence positional term weighting (IPTW) and the Patched language model (PLM), and showed their effectiveness by conducting automatic and human evaluations,

In accordance with this idea, conventional sentence compression methods employ syntactic parsers .

To maintain the subject-predicate relationship in the compressed sentence and retain fluency without using syntactic parsers , we propose two novel features: intra-sentence positional term weighting (IPTW) and the patched language model (PLM).

superior to conventional sequence-oriented methods that employ syntactic parsers while being about 4.3 times faster.

Moreover, their use of syntactic parsers seriously degrades the decoding speed.

The trained parser produces a full syntactic parse of any sentence, while simultaneously producing logical forms for portions of the sentence that have a semantic representation within the parser’s predicate vocabulary.

Integrating syntactic parsing with semantics has long been a goal of natural language processing and is expected to improve both syntactic and semantic processing.

For example, semantics could help predict the differing prepositional phrase attachments in “I caught the butterfly with the net” and “I caught the butterfly with the spots A joint analysis could also avoid propagating syntactic parsing errors into semantic processing, thereby improving performance.

ideally improve the parser’s ability to solve difficult syntactic parsing problems, as in the examples above.

This paper combines two lines of prior work: broad coverage syntactic parsing with CCG and semantic parsing.

Broad coverage syntactic parsing with CCG has produced both resources and successful parsers.

The parser presented in this paper can be viewed as a combination of both a broad coverage syntactic parser and a semantic parser trained using distant supervision.

We have presented our attempts to evaluate syntactic parsers and their representations that are based on different frameworks; dependency parsing, phrase structure parsing, or deep parsing.

(2006) do not rely on syntactic parsing , While the former applied SVMs with kernels on surface strings and the latter is similar to our baseline method.

Parsing technologies have improved considerably in the past few years, and high-performance syntactic parsers are no longer limited to PCFG—based frameworks (Charniak, 2000; Klein and Manning, 2003; Charniak and Johnson, 2005 ; Petrov and Klein, 2007), but also include dependency parsers (McDonald and Pereira, 2006; Nivre and Nilsson, 2005; Sagae and Tsujii, 2007) and deep parsers (Kaplan et al., 2004; Clark and Curran, 2004; Miyao and Tsujii, 2008).

However, efforts to perform extensive comparisons of syntactic parsers based on different frameworks have been limited.

In this paper, we present a comparative evaluation of syntactic parsers and their output representations based on different frameworks: dependency parsing, phrase structure parsing, and deep parsing.

Though the evaluation of syntactic parsers has been a major concern in the parsing community, and a couple of works have recently presented the comparison of parsers based on different frameworks, their methods were based on the comparison of the parsing accuracy in terms of a certain intermediate parse representation (Ringger et al., 2004; Kaplan et al., 2004; Briscoe and Carroll, 2006; Clark and Curran, 2007; Miyao et al., 2007; Clegg and Shepherd, 2007; Pyysalo et al., 2007b; Pyysalo et al., 2007a; Sagae et al., 2008).

We propose Symbol-Refined Tree Substitution Grammars (SR-TSGs) for syntactic parsing .

Syntactic parsing has played a central role in natural language processing.

tree fragments and symbol refinement work complementarily for syntactic parsing .

In this paper, we propose Symbol-Refined Tree Substitution Grammars (SR-TSGs) for syntactic parsing .

In this section, we propose Symbol-Refined Tree Substitution Grammars (SR-TSGs) for syntactic parsing .

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.

The ranking model is automatically derived from word aligned parallel data with a syntactic parser for source language based on both lexical and syntactical features.

The most notable solution to this problem is adopting syntaX-based SMT models, especially methods making use of source side syntactic parse trees.

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.

In this paper, we continue this line of work and address the problem of word reordering based on source syntactic parse trees for SMT.

The algorithm makes use of a fully unsupervised syntactic parser , using its output in order to detect clauses and gather candidate argument collocation statistics.

The recent availability of unsupervised syntactic parsers has offered an opportunity to conduct research on SRL, without reliance on supervised syntactic annotation.

Using VerbNet along with the output of a rule-based chunker (in 2004) and a supervised syntactic parser (in 2005), they spot instances in the corpus that are very similar to the syntactic patterns listed in VerbNet.

Clause information has been applied to accelerating a syntactic parser (Glaysher and Moldovan, 2006).

A transition-based framework with global learning and beam search decoding (Zhang and Clark, 2011) has been applied to a number of natural language processing tasks, including word segmentation, PCS-tagging and syntactic parsing (Zhang and Clark, 2010; Huang and Sagae, 2010; Bohnet and Nivre, 2012; Zhang et al., 2013).

Both are crucial to well-established features for word segmentation, PCS-tagging and syntactic parsing .

(2013) was the first to perform Chinese syntactic parsing over characters.

Second, word internal structures can also be useful for syntactic parsing .

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).

Since the syntactic parses of the tuning and test data contain 29 types of constituent labels and 35 types of POS tags, we have 29 types of XP+ features and 64 types of XP= features.

The reordering rules were either manually designed (Collins et al., 2005; Wang et al., 2007; Xu et al., 2009; Lee et al., 2010) or automatically learned (Xia and McCord, 2004; Gen-zel, 2010; Visweswariah et al., 2010; Khalilov and Sima’an, 2011; Lerner and Petrov, 2013), using syntactic parses .

(2012) obtained word order by using a reranking approach to reposition nodes in syntactic parse trees.

The other two types of features which are related to length and syntactic parsing , only promote the performance slightly.

Since the RST tree is similar to the constituency based syntactic tree except that the constituent nodes are different, the syntactic parsing techniques have been borrowed for discourse parsing (Soricut and Marcu, 2003; Baldridge and Lascarides, 2005; Sagae, 2009; Hernault et al., 2010b; Feng and Hirst, 2012).

Since such a hierarchical discourse tree is analogous to a constituency based syntactic tree except that the constituents in the discourse trees are text spans, previous researches have explored different constituency based syntactic parsing techniques (eg.

First, it is difficult to design a set of production rules as in syntactic parsing , since there are no determinate generative rules for the interior text spans.

We present a novel technique for semantic frame identification using distributed representations of predicates and their syntactic context; this technique leverages automatic syntactic parses and a generic set of word embeddings.

combination of two syntactic parsers as input.

Formally, let cc represent the actual sentence with a marked predicate, along with the associated syntactic parse tree; let our initial representation of the predicate context be Suppose that the word embeddings we start with are of dimension n. Then 9 is a function from a parsed sentence cc to Rm“, where k is the number of possible syntactic context types.

We could represent the syntactic context of runs as a vector with blocks for all the possible dependents warranted by a syntactic parser ; for example, we could assume that positions 0 .

Our parser jointly performs word segmentation, POS tagging and syntactic parsing .

The results also demonstrate that the annotated word structures are highly effective for syntactic parsing , giving an absolute improvement of 0.82% in phrase-structure parsing accuracy over the joint model with flat word structures.

Words are treated as the atomic units in syntactic parsing , machine translation, question answering and other NLP tasks.

In this paper, we investigate Chinese syntactic parsing with character—level information by extending the notation of phrase-structure

Table 1: Argument clustering performance with gold argument identification and gold syntactic parses on CoNLL 2008 shared-task dataset.

We report the results using gold argument identification and gold syntactic parses in order to focus the evaluation on the argument labeling stage and to minimize the noise due to automatic syntactic annotations.

Table 2: Results on CONLL 2009 with automatic argument identification and automatic syntactic parses .

Learning in the context of multiple languages simultaneously has been shown to be beneficial to a number of NLP tasks from morphological analysis to syntactic parsing (Kuhn, 2004; Snyder and Barzilay, 2010; McDonald et al., 2011).

In order to generate such features, much NLP work has to be done beforehand, such as tweet normalization, POS tagging, word stemming, and syntactic parsing .

For syntactic parsing we use a Maximum Spanning Tree dependency parser (McDonald et al., 2005).

For instance, in the second example, using syntactic parsing , we know that “Windows 7” is connected to “better” by a copula, while “Vista” is connected to “better” by a preposition.

In this paper, we rely on the syntactic parse tree to satisfy this need.

To handle this problem, several methods exploited syntactic information, where several heuristic patterns based on syntactic parsing were designed (Popescu and Etzioni, 2005; Qiu et al., 2009; Qiu et al., 2011).

Without using syntactic parsing , the noises from parsing errors can be effectively avoided.

As mentioned in (Liu et al., 2013), using PSWAM can not only inherit the advantages of WAM: effectively avoiding noises from syntactic parsing errors when dealing with informal texts, but also can improve the mining performance by using partial supervision.

Upon manual inspection, many of the remaining errors are due to syntactic parsing errors that are too severe to fix.

This is partly due to the fact that ATIS sentences are out of domain compared to the newswired text on which the syntactic parsers were trained.

However, in complex sentences, syntax and semantic often diverge, either due to their differing goals or simply stemming from syntactic parsing errors.

In addition, we present results for a full system using additional features which achieves close to state of the art performance without resorting to gold syntactic parses or to context outside the relation.

In addition, we present a system which combines these word pairs with additional features to achieve near state of the art performance without the use of syntactic parse features and of context outside the arguments of the relation.

1Reliable syntactic parses are not always available in domains other than newswire, and context (preceding relations, especially explicit relations) is not always available in some applications such as generation and question answering.

Seeking alternatives to measuring syntactic complexity of spoken responses via syntactic parsers , we study a shallow-analysis based approach for use in automatic scoring.

Not surprisingly, Chen and Zechner (2011) studied measures of grammatical complexity via syntactic parsing and found that a Pearson’s correlation coefficient of 0.49 between syntactic complexity measures (derived from manual transcriptions) and proficiency scores, was drastically reduced to near nonexistence when the measures were applied to ASR word hypotheses.

The measures of syntactic complexity in this approach are POS bigrams and are not obtained by a deep analysis ( syntactic parsing ) of the structure of the sentence.

In Section 3.1, we introduced pipeline-trained models for SRL, which used grammar induction to predict unlabeled syntactic parses .

In this simple pipeline, the first stage syntactically parses the corpus, and the second stage predicts semantic predicate-argument structure for each sentence using the labels of the first stage as features.

In our low-resource pipelines, we assume that the syntactic parser is given no labeled parses—however, it may optionally utilize the semantic parses as distant supervision.

This paper describes a novel technique for incorporating syntactic knowledge into phrase-based machine translation through incremental syntactic parsing .

Syntactic parsing may help produce more grammatical output by better modeling structural relationships and long-distance dependencies.

We directly integrate incremental syntactic parsing into phrase-based translation.

In this work, we parse our English data, and for each sentence E = e’f, let T be its syntactic parse .

(1) that using the structure of l-best English syntactic parse trees is a reasonable way to frame and drive our search, and (2) that F-measure approximately decomposes over hyperedges.

Initial alignments We can construct a word alignment hierarchically, bottom-up, by making use of the structure inherent in syntactic parse trees.

Experiment on the SANCL 2012 shared task show that our approach achieves 93.15% average tagging accuracy, which is the best accuracy reported so far on this data set, higher than those given by ensembled syntactic parsers .

For future work, we would like to investigate the two-phase approach to more challenging tasks, such as web domain syntactic parsing .

set, higher than those given by ensembled syntactic parsers .

The applications range from simple classification tasks such as text classification and history-based tagging (Ratnaparkhi, 1996) to more complex structured prediction tasks such as part-of-speech (POS) tagging (Lafferty et al., 2001), syntactic parsing (Clark and Curran, 2004) and semantic role labeling (Toutanova et al., 2005).

SGD was recently used for NLP tasks including machine translation (Tillmann and Zhang, 2006) and syntactic parsing (Smith and Eisner, 2008; Finkel et al., 2008).

The model can be used for tasks like syntactic parsing (Finkel et al., 2008) and semantic role labeling (Cohn and Blunsom, 2005).