Processing Unknown Words in HPSG

Processing Unknown Words in HPSG Petra Barg and Markus Walther* Seminar ftir Allgemeine Sprachwissenschaft Heinrich-Heine-Universit~it Dtisseldorf Universit~itsstr. 1, D-40225 Dtisseldorf, Germany {barg, walther}@ling, uni-duesseldor f. de Abstract The lexical acquisition system presented in this pa- per incrementally updates linguistic properties of un- known words inferred from their surrounding con- text by parsing sentences with an HPSG grammar for German. We employ a gradual, information- based concept of "unknownness" providing a uni- form treatment for the range of completely known to maximally unknown lexical entries. "Unknown" in- formation is viewed as revisable information, which is either generalizable or specializable. Updating takes place after parsing, which only requires a mod- ified lexical lookup. Revisable pieces of informa- tion are identified by grammar-specified declarations wlfich provide access paths into the parse feature structure. The updating mechanism revises the cor- responding places in the lexical feature structures iff the context actually provides new information. For revising generalizable inlbrmation, type union is re- quired. A worked-out example demonstrates the in- ferential capacity of our implemented system. 1 Introduction It is a remarkable fact that humans can often un- derstand sentences containing unknown words, in- fer their grammatical properties and incrementally refine hypotheses about these words when encoun- tering later instances. In contrast, many current NLP systems still presuppose a complete lexicon. Notable exceptions include Zernik (1989), Erbach (1990), Hastings & Lytinen (1994). See Zernik for an intro- duction to the general issues involved. This paper describes an HPSG-based system which can incrementally learn and refine proper- ties of unknown words after parsing individual sen- *This work was carried out within the Sonderforschungs- bereich 282 "Theorie des Lexikons' (project B3), funded by the German Federal Research Agency DFG. We thank James Kil- bury and members of the B3 group for fruitful discussion. tences. It focusses on extracting linguistic proper- ties, as compared to e.g. general concept learning (Hahn, Klenner & Schnattinger 1996). Unlike Er- bach (1990), however, it is not confined to sim- ple morpho-syntactic information but can also han- dle selectional restrictions, semantic types and argu- ment structure. Finally, while statistical approaches like Brent (1991) can gather e.g. valence informa- tion from large corpora, we are more interested in full grammatical processing of individual sentences to maximally exploit each context. The following three goals serve to structure our model. It should i) incorporate a gradual, information-based conceptualization of "unknown- ness". Words are not unknown as a whole, but may contain unlmown, i.e. revisable pieces of infor- mation. Consequently, even known words can un- dergo revision to e.g. acquire new senses. This view replaces the binary distinction between open and closed class words. It should ii) maximally exploit the rich representations and modelling conventions of HPSG and associated formalisms, with essen- tially the same grammar and lexicon as compared to closed-lexicon approaches. This is important both to facilitate reuse of existing grammars and to en- able meaningful feedback for linguistic theorizing. Finally, it should iii) possess domain-independent in- ference and lexicon-updating capabilities. The gram- mar writer …

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