Computer Science – Computation and Language
Scientific paper
2007-09-15
In Proceedings of the ACL-SIGLEX 2005 Workshop on Deep Lexical Acquisition, Ann Arbor, USA, pp. 67-76
Computer Science
Computation and Language
Scientific paper
We propose a range of deep lexical acquisition methods which make use of morphological, syntactic and ontological language resources to model word similarity and bootstrap from a seed lexicon. The different methods are deployed in learning lexical items for a precision grammar, and shown to each have strengths and weaknesses over different word classes. A particular focus of this paper is the relative accessibility of different language resource types, and predicted ``bang for the buck'' associated with each in deep lexical acquisition applications.
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