A corpus-trained parser for systemic-functional syntax

This thesis presents a language engineering approach to the development of a tool for the parsing of relatively unrestricted English text, as found in spoken natural language corpora.

Parsing unrestricted English requires large-scale lexical and grammatical resources, and an algorithm for combining the two to assign syntactic structures to utterances of the language. The grammatical theory adopted for this purpose is systemic functional grammar (SFG), despite the fact that it is traditionally used for natural language generation. The parser will use a probabilistic systemic functional syntax (Fawcett 1981, Souter 1990), which was originally employed to hand-parse the Polytechnic of Wales corpus (Fawcett and Perkins 1980, Souter 1989), a 65,000 word transcribed corpus of children's spoken English. Although SFG contains mechanisms for representing semantic as well as syntactic choice in NL generation, the work presented here focuses on the parallel task of obtaining syntactic structures for sentences, and not on retrieving a full semantic interpretation.

The syntactic language model can be extracted automatically from the Polytechnic of Wales corpus in a number of formalisms, including 2,800 simple context-free rules (Souter and Atwell 1992). This constitutes a very large formal syntax language, but still contains gaps in its coverage. Some of these are accounted for by a mechanism for expanding the potential for co-ordination and subordination beyond that observed in the corpus. However, at the same time the set of syntax rules can be reduced in size by allowing optionality in the rules. Alongside the context-free rules (which capture the largely horizontal relationships between the mother and daughter constituents in a tree), a vertical trigram model is extracted from the corpus, controlling the vertical relationships between possible grandmothers, mothers and daughters in the parse tree, which represent the alternating layers of elements of structure and syntactic units in SFG. Together, these two models constitute a quasi-context-sensitive syntax.

A probabilistic lexicon also extracted from the POW corpus proved inadequate for unrestricted English, so two alternative part-of-speech tagging approaches were investigated. Firstly, the CELEX lexical database was used to provide a large-scale word tagging facility. To make the lexical database compatible with the corpus-based grammar, a hand-crafted mapping was applied to the lexicon's theory neutral grammatical description. This transformed the lexical tags into systemic functional grammar labels, providing a harmonised probabilistic lexicon and grammar. Using the CELEX lexicon, the parser has to do the work of lexical disambiguation. This overhead can be removed with the second approach: The Brill tagger trained on the POW corpus can be used to assign unambiguous labels (with over 92% success rate) to the words to be parsed. While tagging errors do compromise the success rate of the parser, these are outweighed by the search time saved by introducing only one tag per word.

A probabilistic chart parsing program which integrated the reduced context-free syntax, the vertical trigram model, with either the SFG lexicon or the POW trained Brill tagger was implemented and tested on a sample of the corpus. Without the vertical trigram model and using CELEX lexical look-up, results were extremely poor, with combinatorial explosion in the syntax preventing any analyses being found for sentences longer than five words within a practical time span. The seemingly unlimited potential for vertical recursion in a context-free rule model of systemic functional syntax is a severe problem for a standard chart parser. However, with addition of the Brill tagger and vertical trigram model, the performance is markedly improved. The parser achieves a reasonably creditable success rate of 76%, if the criteria for success are liberally set at at least one legitimate SF syntax tree in the first six produced for the given test data. While the resulting parser is not suitable for real-time applications, it demonstrates the potential for the use of corpus-derived probabilistic syntactic data in parsing relatively unrestricted natural language, including utterances with ellipted elements, unfinished constituents, and constituents without a syntactic head. With very large syntax models of this kind, the problem of multiple solutions is common, and the modified chart parser presented here is able to produce correct or nearly correct parses in the first few it finds.

Apart from the implementation of a parser for systemic functional syntax, the re-usable method by which the lexical look-up, syntactic and parsing resources were obtained is a significant contribution to the field of computational linguistics.