Transformation-Based Learning For Semantic Parsing
INTERSPEECH 2009: 10TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION 2009, VOLS 1-5(2009)
摘要
This paper presents a semantic parser that transforms an initial semantic hypothesis into the correct semantics by applying an ordered list of transformation rules. These rules are learnt automatically from a training corpus with no prior linguistic knowledge and no alignment between words and semantic concepts. The learning algorithm produces a compact set of rules which enables the parser to be very efficient while retaining high accuracy. We show that this parser is competitive with respect to the state-of-the-art semantic parsers on the ATIS and TownInfo tasks.
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关键词
spoken language understanding, semantics, natural language processing, transformation-based learning
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