VENUS: two-phase machine translation system
Future Generation Computer Systems - Special issue on machine translation
Long sentence analysis by domain-specific pattern grammar
EACL '93 Proceedings of the sixth conference on European chapter of the Association for Computational Linguistics
Machine Learning for Information Extraction in Informal Domains
Machine Learning - Special issue on information retrieval
Information classification and navigation based on 5W1H of the target information
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
TIPSTER '96 Proceedings of a workshop on held at Vienna, Virginia: May 6-8, 1996
Pattern matching and discourse processing in information extraction from Japanese text
Journal of Artificial Intelligence Research
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NEC Corporation has had years of experience in natural language processing and machine translation[1, 2, 3, 4, 5], and currently markets commercial natural language processing systems. Utilizing dictionaries and parsing engines we have already had, we have developed the VENIEX System (VENus for Information EXtraction) as used for MUC-5 in only three months. Our method is to apply both domain-specific keyword-based analysis and full sentential parsing with general grammar[6, 7]. The keyword dictionary of VENIEX contains about thirty thousand entries, whose semantic structures are sub_ME_Capability frame, and the parsing and discourse processing are controlled with the information given in this semantic structure of keywords. The resulting scores of VENIEX for formal run texts were from 0.7181 (minimum) to 0.7548 (maximum) in Richness-Normalized Error and 48.33 in F-MEASURES (P&R).