This paper investigates perceptron training for a wide-coverage CCG parser and compares the perceptron with a log-linear model. The CCG parser uses a phrase-structure parsing model and dynamic programming in the form of the Viterbi algorithm to find the highest scoring derivation. The difficulty in using the perceptron for a phrase-structure parsing model is the need for an efficient decoder. W...

We evaluate the statistical dependency parser, Malt, on a new dataset of sentences taken from tweets. We use a version of Malt which is trained on gold standard phrase structure Wall Street Journal (WSJ) trees converted to Stanford labelled dependencies. We observe a drastic drop in performance moving from our in-domain WSJ test set to the new Twitter dataset, much of which has to do with the p...

Although phrase structure grammars have turned out to be a more popular approach for analysis and representation of the natural language syntactic structures, dependency grammars are often considered as being more appropriate for free word order languages. While building a parser for Latvian, a language with a rather free word order, we found (similarly to TIGER project for German and Talbanken...

We describe a method for enriching the output of a parser with information available in a corpus. The method is based on graph rewriting using memorybased learning, applied to dependency structures. This general framework allows us to accurately recover both grammatical and semantic information as well as non-local dependencies. It also facilitates dependency-based evaluation of phrase structur...

Word dependency is important in parsing technology. Some applications such as Information Extraction from biological documents benefit from word dependency analysis even without phrase labels. Therefore, we expect an accurate dependency analyzer trainable without using phrase labels is useful. Although such an English word dependency analyzer was proposed by Yamada and Matsumoto, its accuracy i...

This paper presents and evaluates a phonological phrase parser for a Spanish text-to-speech system. The parser consists of three stages: 1) lexical lookup, using a small dictionary (428 words); 2) preliminary phrase boundary placement, using a modification of Liberman and Church’s (1992) function group parser; and 3) readjustment of phrase boundaries, using syllable count and punctuation. A cor...

Dependency structures do not have the information of phrase categories in phrase structure grammar. Thus, dependency parsing relies heavily on the lexical information of words. This paper discusses our investigation into the effectiveness of lexicalization in dependency parsing. Specifically, by restricting the degree of lexicalization in the training phase of a parser, we examine the change in...

First, we believe that the phrase structure used by many (if not most) linguistic theories is useful only in deriving the syntactic behavior of lexical heads (or classes of lexical heads) from more general principles(which is of course the goal of syntactic theory); once derived, the syntactic behavior can be expressed in simpler terms that can be easier to manipulate in a computational setting...

This paper introduces a paradigm of context-dependent grammar (CDG) and an acquisition system that, through interactive teaching sessions, accumulates the CDG rules. The resulting context-sensitive rules are used by a stack-based, shift~reduce parser to compute unambiguous syntactic structures of sentences. The acquisition system and parser have been applied to the phrase structure and case ana...

In this paper we present a novel phrase structure parsing approach with the help of dependency structure. Different with existing phrase parsers, in our approach the inference procedure is guided by dependency structure, which makes the parsing procedure flexibly. The experimental results show our approach is much more accurate. With the help of golden dependency trees, F1 score of our parser a...