Transformation-based learning for semantic parsing
Filip Jurčíček, Milomir M. Gašić, Simon Keizer, François Mairesse, B. Thomson, Kai Yu, S.J. Young · 2009
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. Index Terms: spoken language understanding, semantics, natural language processing, transformation-based learning