@article{brooke-etal-2017-unsupervised,
title = "Unsupervised Acquisition of Comprehensive Multiword Lexicons using Competition in an n-gram Lattice",
author = "Brooke, Julian and
{\v{S}}najder, Jan and
Baldwin, Timothy",
editor = "Lee, Lillian and
Johnson, Mark and
Toutanova, Kristina",
journal = "Transactions of the Association for Computational Linguistics",
volume = "5",
year = "2017",
address = "Cambridge, MA",
publisher = "MIT Press",
url = "https://aclanthology.org/Q17-1032",
doi = "10.1162/tacl_a_00073",
pages = "455--470",
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.",
}
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%0 Journal Article
%T Unsupervised Acquisition of Comprehensive Multiword Lexicons using Competition in an n-gram Lattice
%A Brooke, Julian
%A Šnajder, Jan
%A Baldwin, Timothy
%J Transactions of the Association for Computational Linguistics
%D 2017
%V 5
%I MIT Press
%C Cambridge, MA
%F brooke-etal-2017-unsupervised
%X 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.
%R 10.1162/tacl_a_00073
%U https://aclanthology.org/Q17-1032
%U https://doi.org/10.1162/tacl_a_00073
%P 455-470
Markdown (Informal)
[Unsupervised Acquisition of Comprehensive Multiword Lexicons using Competition in an n-gram Lattice](https://aclanthology.org/Q17-1032) (Brooke et al., TACL 2017)
ACL