NDLI: A syntactic language model based on incremental CCG parsing

Content Provider	IEEE Xplore Digital Library
Author	Hassan, H. Sima'an, K. Way, A.
Copyright Year	2008
Description	Author affiliation: Sch. of Comput., Dublin City Univ., Dublin (Hassan, H.; Way, A.) \|\| Language & Computations, Univ. van Amsterdam, Amsterdam (Sima'an, K.)
Abstract	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.
Starting Page	205
Ending Page	208
File Size	117336
Page Count	4
File Format	PDF
ISBN	9781424434718
DOI	10.1109/SLT.2008.4777876
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-12-15
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Natural languages Speech recognition Probability Tin Language Modeling Decoding Large-scale systems Grammar Usability Assembly Delay Context modeling
Content Type	Text
Resource Type	Article

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