Recycling Terms into a Partial Parser

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 online dict ionary. Through F A S T R , 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.