XML-Based Data Preparation for Robust Deep Parsing

XML-Based Data Preparation for Robust Deep Parsing Claire Grover and Alex Lascarides Division of Informatics The University of Edinburgh 2 Buccleuch Place Edinburgh EH8 9LW, UK � C.Grover, A.Lascarides � @ed.ac.uk Abstract We describe the use of XML tokenisa- tion, tagging and mark-up tools to pre- pare a corpus for parsing. Our tech- niques are generally applicable but here we focus on parsing Medline abstracts with the ANLT wide-coverage grammar. Hand-crafted grammars inevitably lack coverage but many coverage failures are due to inadequacies of their lexi- cons. We describe a method of gain- ing a degree of robustness by interfac- ing POS tag information with the exist- ing lexicon. We also show that XML tools provide a sophisticated approach to pre-processing, helping to ameliorate the ‘messiness’ in real language data and improve parse performance. 1 Introduction The field of parsing technology currently has two distinct strands of research with few points of contact between them. On the one hand, there is thriving research on shallow parsing, chunk- ing and induction of statistical syntactic analysers from treebanks; and on the other hand, there are systems which use hand-crafted grammars which provide both syntactic and semantic coverage. ‘Shallow’ approaches have good coverage on cor- pus data, but extensions to semantic analysis are still in a relative infancy. The ‘deep’ strand of research has two main problems: inadequate cov- erage, and a lack of reliable techniques to select the correct parse. In this paper we describe on- going research which uses hybrid technologies to address the problem of inadequate coverage of a ‘deep’ parsing system. In Section 2 we describe how we have modified an existing hand-crafted grammar’s look-up procedure to utilise part-of- speech (POS) tag information, thereby ameliorat- ing the lexical information shortfall. In Section 3 we describe how we combine a variety of existing NLP tools to pre-process real data up to the point where a hand-crafted grammar can start to be use- ful. The work described in both sections is en- abled by the use of an XML processing paradigm whereby the corpus is converted to XML with analysis results encoded as XML annotations. In Section 4 we report on an experiment with a ran- dom sample of 200 sentences which gives an ap- proximate measure of the increase in performance we have gained. The work we describe here is part of a project which aims to combine statistical and symbolic processing techniques to compute lexical seman- tic relationships, e.g. the semantic relations be- tween nouns in complex nominals. We have cho- sen the medical domain because the field of med- ical informatics provides a relative abundance of pre-existing knowledge bases and ontologies. Our efforts so far have focused on the OHSUMED corpus (Hersh et al., 1994) which is a collection of Medline abstracts of medical journal papers.1 While the focus of the project is on seman- tic issues, a prerequisite is a large, reliably an- notated corpus and a level of syntactic process- 1Sager et al. (1994) describe the …

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