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 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).

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).