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A syntactic language model based on incremental CCG parsing

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3 Author(s)
Hassan, H. ; Sch. of Comput., Dublin City Univ., Dublin ; Sima'an, K. ; Way, A.

Syntactically-enriched language models (parsers) constitute a promising component in applications such as machine translation and speech-recognition. To maintain a useful level of accuracy, existing parsers are non-incremental and must span a combinatorially growing space of possible structures as every input word is processed. This prohibits their incorporation into standard linear-time decoders. In this paper, we present an incremental, linear-time dependency parser based on Combinatory Categorial Grammar (CCG) and classification techniques. We devise a deterministic transform of CCG-bank canonical derivations into incremental ones, and train our parser on this data. We discover that a cascaded, incremental version provides an appealing balance between efficiency and accuracy.

Published in:

Spoken Language Technology Workshop, 2008. SLT 2008. IEEE

Date of Conference:

15-19 Dec. 2008