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Constrained Lexical Attraction Models

TitleConstrained Lexical Attraction Models
Publication TypeConference Paper
Year of Publication2006
AuthorsIon, R, Mititelu, VB
Conference Name19th International FLAIRS Conference
PublisherAmerican Association for Artificial Intelligence
Conference LocationMelbourne Beach, Florida

Lexical Attraction Models (LAMs) were first introduced by Deniz Yuret in (Yuret 1998) to exemplify how an algorithm can learn word dependencies from raw text. His general thesis is that lexical attraction is the likelihood of a syntactic relation. However, the lexical attraction acquisition algorithm from (Yuret 1998) does not take into account the morpho-syntactical information provided by a part-of-speech (POS) tagger and, thus, is unable to impose certain linguistically motivated restrictions on the creation of the links. Furthermore, it does not behave well when
encountering unknown words. The present article presents a new link discovery algorithm using the annotation provided by a POS-tagger. The results show an F-measure of approximately 70% when comparing the links produced by this algorithm with those produced by a fully-fledged parser.