A system for text analysis and lexical knowledge acquisition

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One of the major limitations of current NLP systems is a poor encoding of lexical knowledge (morphologic lexicon, grammar, and semantic dictionary). This paper describes a high-coverage system, DANTE, for natural language processing and query-answering. At the current state of implementation, the morphological analyzer provides 100% coverage over the corpus (5000 press agency releases with about 100,000 different words) and the parser can analyze 80% of the sentences correctly. A semantic lexicon provides a detailed case-based representation of word senses. The morphologic lexicon (10,000 elementary lemmata plus affixes and suffixes) and the grammar (100 rules) was manually entered; during the first phase of the DANTE project, the semantic knowledge was also manullly encoded. More recently, a methodology for semi-automatic acquisition of a case-based semantic lexicon has been devised.

论文关键词:Natural language,Text understanding,Knowledge acquisition,Query answering

论文评审过程:Author links open overlay panelF.AntonaciM.RussoM.T.PazienzaP.Velardi

论文官网地址:https://doi.org/10.1016/0169-023X(89)90002-5