Can Subcategorisation Probabilities Help a Statistical Parser?

Research into the automatic acquisition of lexical information from corpora is starting to produce large-scale computational lexicons containing data on the relative frequencies of subcategorisation alternatives for individual verbal predicates. However, the empirical question of whether this type of frequency information can in practice improve the accuracy of a statistical parser has not yet been answered. In this paper we describe an experiment with a wide-coverage statistical grammar and parser for English and subcategorisation frequencies acquired from ten million words of text which shows that this information can significantly improve parse accuracy.Comment: 9 pages, uses colacl.st

Data-Oriented Language Processing. An Overview

During the last few years, a new approach to language processing has started to emerge, which has become known under various labels such as "data-oriented parsing", "corpus-based interpretation", and "tree-bank grammar" (cf. van den Berg et al. 1994; Bod 1992-96; Bod et al. 1996a/b; Bonnema 1996; Charniak 1996a/b; Goodman 1996; Kaplan 1996; Rajman 1995a/b; Scha 1990-92; Sekine & Grishman 1995; Sima'an et al. 1994; Sima'an 1995-96; Tugwell 1995). This approach, which we will call "data-oriented processing" or "DOP", embodies the assumption that human language perception and production works with representations of concrete past language experiences, rather than with abstract linguistic rules. The models that instantiate this approach therefore maintain large corpora of linguistic representations of previously occurring utterances. When processing a new input utterance, analyses of this utterance are constructed by combining fragments from the corpus; the occurrence-frequencies of the fragments are used to estimate which analysis is the most probable one. In this paper we give an in-depth discussion of a data-oriented processing model which employs a corpus of labelled phrase-structure trees. Then we review some other models that instantiate the DOP approach. Many of these models also employ labelled phrase-structure trees, but use different criteria for extracting fragments from the corpus or employ different disambiguation strategies (Bod 1996b; Charniak 1996a/b; Goodman 1996; Rajman 1995a/b; Sekine & Grishman 1995; Sima'an 1995-96); other models use richer formalisms for their corpus annotations (van den Berg et al. 1994; Bod et al., 1996a/b; Bonnema 1996; Kaplan 1996; Tugwell 1995).Comment: 34 pages, Postscrip

Probabilistic Parsing Strategies

We present new results on the relation between purely symbolic context-free parsing strategies and their probabilistic counter-parts. Such parsing strategies are seen as constructions of push-down devices from grammars. We show that preservation of probability distribution is possible under two conditions, viz. the correct-prefix property and the property of strong predictiveness. These results generalize existing results in the literature that were obtained by considering parsing strategies in isolation. From our general results we also derive negative results on so-called generalized LR parsing.Comment: 36 pages, 1 figur

A New Statistical Parser Based on Bigram Lexical Dependencies

This paper describes a new statistical parser which is based on probabilities of dependencies between head-words in the parse tree. Standard bigram probability estimation techniques are extended to calculate probabilities of dependencies between pairs of words. Tests using Wall Street Journal data show that the method performs at least as well as SPATTER (Magerman 95, Jelinek et al 94), which has the best published results for a statistical parser on this task. The simplicity of the approach means the model trains on 40,000 sentences in under 15 minutes. With a beam search strategy parsing speed can be improved to over 200 sentences a minute with negligible loss in accuracy.Comment: 8 pages, to appear in Proceedings of ACL 96. Uuencoded gz-compressed postscript file created by csh script uufile

Wide-coverage deep statistical parsing using automatic dependency structure annotation

A number of researchers (Lin 1995; Carroll, Briscoe, and Sanfilippo 1998; Carroll et al. 2002; Clark and Hockenmaier 2002; King et al. 2003; Preiss 2003; Kaplan et al. 2004;Miyao and Tsujii 2004) have convincingly argued for the use of dependency (rather than CFG-tree) representations for parser evaluation. Preiss (2003) and Kaplan et al. (2004) conducted a number of experiments comparing “deep” hand-crafted wide-coverage with “shallow” treebank- and machine-learning based parsers at the level of dependencies, using simple and automatic methods to convert tree output generated by the shallow parsers into dependencies. In this article, we revisit the experiments in Preiss (2003) and Kaplan et al. (2004), this time using the sophisticated automatic LFG f-structure annotation methodologies of Cahill et al. (2002b, 2004) and Burke (2006), with surprising results. We compare various PCFG and history-based parsers (based on Collins, 1999; Charniak, 2000; Bikel, 2002) to find a baseline parsing system that fits best into our automatic dependency structure annotation technique. This combined system of syntactic parser and dependency structure annotation is compared to two hand-crafted, deep constraint-based parsers (Carroll and Briscoe 2002; Riezler et al. 2002). We evaluate using dependency-based gold standards (DCU 105, PARC 700, CBS 500 and dependencies for WSJ Section 22) and use the Approximate Randomization Test (Noreen 1989) to test the statistical significance of the results. Our experiments show that machine-learning-based shallow grammars augmented with sophisticated automatic dependency annotation technology outperform hand-crafted, deep, widecoverage constraint grammars. Currently our best system achieves an f-score of 82.73% against the PARC 700 Dependency Bank (King et al. 2003), a statistically significant improvement of 2.18%over the most recent results of 80.55%for the hand-crafted LFG grammar and XLE parsing system of Riezler et al. (2002), and an f-score of 80.23% against the CBS 500 Dependency Bank (Carroll, Briscoe, and Sanfilippo 1998), a statistically significant 3.66% improvement over the 76.57% achieved by the hand-crafted RASP grammar and parsing system of Carroll and Briscoe (2002)