Using descriptions of trees in a tree adjoining grammar
Computational Linguistics
Retrieving terms and their variants in a lexicalized unification-based framework
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Selection and information: a class-based approach to lexical relationships
Selection and information: a class-based approach to lexical relationships
Text-Based Intelligent Systems: Current Research and Practice in Information Extraction and Retrieval
An endogeneous corpus-based method for structural noun phrase disambiguation
EACL '93 Proceedings of the sixth conference on European chapter of the Association for Computational Linguistics
Parsing idioms in lexicalized TAGs
EACL '89 Proceedings of the fourth conference on European chapter of the Association for Computational Linguistics
A logical semantics for feature structures
ACL '86 Proceedings of the 24th annual meeting on Association for Computational Linguistics
A logical version of functional grammar
ACL '87 Proceedings of the 25th annual meeting on Association for Computational Linguistics
Optimizing the computational lexicalization of large grammars
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
Information retrieval using robust natural language processing
ACL '92 Proceedings of the 30th annual meeting on Association for Computational Linguistics
COLING '92 Proceedings of the 14th conference on Computational linguistics - Volume 4
Expansion of multi-word terms for indexing and retrieval using morphology and syntax
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
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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 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.