Unsupervised Acquisition of Comprehensive Multiword Lexicons using Competition in an n-gram Lattice

ACL 2018

Unsupervised Acquisition of Comprehensive Multiword Lexicons using Competition in an n-gram Lattice

Jun 29, 2018
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Abstract: We present a new model for acquiring comprehensive multiword lexicons from large corpora based on competition among n-gram candidates. In contrast to the standard approach of simple ranking by association measure, in our model n-grams are arranged in a lattice structure based on subsumption and overlap relationships, with nodes inhibiting other nodes in their vicinity when they are selected as a lexical item. We show how the configuration of such a lattice can be optimized tractably, and demonstrate using annotations of sampled n-grams that our method consistently outperforms alternatives by at least 0.05 F-score across several corpora and languages. Authors: Julian Brooke, Jan Snajder, Timothy Baldwin (The University of Melbourne, University of Zagreb)

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