A Text Understander that Learns

We introduce an approach to the automatic acquisition of new concepts from natural language texts which is tightly integrated with the underlying text understanding process. The learning model is centered around the 'quality' of different forms of linguistic and conceptual evidence which underlies the incremental generation and refinement of alternative concept hypotheses, each one capturing a different conceptual reading for an unknown lexical item.

• Publication year

  1998

• Venue

  Annual Meeting of the Association for Computational Linguistics

• Publication date

  1998-08-10

• Fields of study

  Linguistics, Computer Science

• Identifiers
  DOI 10.3115/980845.980926
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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