Recycling Terms into a Partial Parser

Recycling Terms into a Partial Parser Christian Jacquemin Institut de Recherche en Informatique de Nantes (IRIN) IUT de Nantes 3, rue du Mar6chal Joffre F-44041 NANTES Cedex 01 - FRANCE jaequemin @ irin.iut-nantos.univ-nantos.fr Abstract Both full-text information retrieval and large scale parsing require text preprocessing to identify strong lexical associations in textual databases. In order to associate linguistic felicity with computational efficiency, we have conceived FASTR a unification-based parser supporting large textual and grammatical databases. The grammar is composed of term rules obtained by tagging and lemmatizing term lists with an on- line dictionary. Through FASTR, large terminological data can be recycled for text processing purposes. Great stress is placed on the handling of term variations through metarules which relate basic terms to their semantically close morphosyntactic variants. The quality of terminological extraction and the computational efficiency of FASTR are evaluated through a joint experiment with an industrial documentation center. The processing of two large technical corpora shows that the application is scalable to such industrial data and that accounting for term variants results in an increase of recall by 20%. Although automatic indexing is the most straightforward application of FASTR, it can be extended fruitfully to terminological acquisition and compound interpretation. Introduction Large terminological databases are now available and can be used as lexicons in Natural Language Processing (NLP) systems aimed at terminology extraction. In FASTR term lists are transformed into large lexicalized grammars and are parsed with a robust and computationally tractable unification-based parser. Our method contrasts with pattern-matching techniques by offering an expressive and convenient descriptive framework. It also differs from a general multipurpose parser by an ability to recycle linguistic knowledge embodied in terminological data. Higher quality in terminological extraction is achieved thanks to a description of term variations. Areas of application using such a tool for terminology extraction include automatic indexing through an assignment of text pointers to thesaurus entries, knowledge acquisition form textual databases, noun phrase structural disambiguation and machine translation with a specific concern for the translation of idioms, compounds and terms. When designing any NLP system with large linguistic resources, there is tension between tractability and descriptive power. Finite state automata are efficient tools for lexical extraction. But their lack of convenience for information description makes the testing of different methodological choices difficult. Such a limitation is specifically problematic during the development stage. Symmetrically, unification-based parsers offer rich and conceptually tractable formalisms, but their computational cost is very high. The approach taken in FASTR is to use a convenient grammatical description stemming from PATR-H (Shieber 1986) associated with an optimized computational engine. Efficiency and constraint-based grammar formalism have motivated the acronym of the application (FAST + PATR-H) that stands for FAST TERM RECOGNIZER. When terminology extraction is applied to automatic indexing, two measures are important: recall and precision. Precision is crucial for applications using acquisition methods which are subject to an excessive recall, blurring terminological entries with syntactic recurrences or semantic preferences. Conversely, in a knowledge-based method like FASTR, recall is a …

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