Developing a hybrid NP parser

Developing a hybrid NP parser Atro Voutilainen Department of General Linguistics P.O. Box 4 FIN-00014 University of Helsinki Finland avout ila@ling, helsinki, fi Llufs Padr6 Dept. Llenguatges i Sistemes InformS, tics Universitat Polit~cnica de Catalunya C/Grail Capit~ s/n. 08034 Barcelona Catalonia padro@lsi, upc. es Abstract We describe the use of energy function op- timisation in very shallow syntactic pars- ing. The approach can use linguistic rules and corpus-based statistics, so the strengths of both linguistic and statisti- cal approaches to NLP can be combined in a single framework. The rules are con- textual constraints for resolving syntactic ambiguities expressed as alternative tags, and the statistical language model consists of corpus-based n-grams of syntactic tags. The success of the hybrid syntactic dis- ambiguator is evaluated against a held-out benchmark corpus. Also the contributions of the linguistic and statistical language models to the hybrid model are estimated. 1 Introduction The language models used by natural language an- alyzers are traditionally based on two approaches. In the linguistic approach, the model is based on hand-crafted rules derived from the linguist's gen- eral and/or corpus-based knowledge about the ob- ject language. In the data-driven approach, the model is automatically generated from annotated text corpora, and the model can be represented e.g. as n-grams (Garside et al., 1987), local rules (Hindle, 1989) or neural nets (Schmid, 1994). Most hybrid approaches combine statistical infor- mation with automatically extracted rule-based in- formation (Brill, 1995; Daelemans et al., 1996). Rel- atively little attention has been paid to models where the statistical approach is combined with a truly lin- guistic model (i.e. one generated by a linguist). This paper reports one such approach: syntactic rules written by a linguist are combined with statistical information using the relaxation labelling algorithm. 80 Our application is very shallow parsing: identifi- cation of verbs, premodifiers, nominal and adverbial heads, and certain kinds of postmodifiers. We call this parser a noun phrase parser. The input is English text morphologically tagged with a rule-based tagger called EngCG (Voutilainen et al., 1992; Karlsson et al., 1995). Syntactic word- tags are added as alternatives (e.g. each adjective gets a premodifier tag, postmodifier tag and a nomi- nal head tag as alternatives). The system should re- move contextually illegitimate tags and leave intact each word's most appropriate tag. In other words, the syntactic language model is applied by a disam- biguator. The parser has a recall of 100% if all words retain the correct morphological and syntactic reading; the system's precision is 100% if the output contains no illegitimate morphological or syntactic readings. In practice, some correct readings are discarded, and some ambiguities remain unresolved (i.e. some words retain two or more alternative analyses). The system can use linguistic rules and corpus- based statistics. Notable about the system is that minimal human effort was needed for creating its language models (the linguistic consisting of syn- tactic disambiguation rules based on the Constraint Grammar framework (Karlsson, 1990; Karlsson et al., 1995); the corpus-based consisting of bigrams and trigrams): Only …

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