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        <title>Unsupervised Mining of Analogical Frames by Constraint Satisfaction</title>
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        <namePart type="given">Lance</namePart>
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    <abstract>It has been demonstrated that vector-based representations of words trained on large text corpora encode linguistic regularities that may be exploited via the use of vector space arithmetic. This capability has been extensively explored and is generally measured via tasks which involve the automated completion of linguistic proportional analogies. The question remains, however, as to what extent it is possible to induce relations from word embeddings in a principled and systematic way, without the provision of exemplars or seed terms. In this paper we propose an extensible and efficient framework for inducing relations via the use of constraint satisfaction. The method is efficient, unsupervised and can be customized in various ways. We provide both quantitative and qualitative analysis of the results.</abstract>
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%0 Conference Proceedings
%T Unsupervised Mining of Analogical Frames by Constraint Satisfaction
%A De Vine, Lance
%A Geva, Shlomo
%A Bruza, Peter
%Y Kim, Sunghwan Mac
%Y Zhang, Xiuzhen (Jenny)
%S Proceedings of the Australasian Language Technology Association Workshop 2018
%D 2018
%8 December
%C Dunedin, New Zealand
%F de-vine-etal-2018-unsupervised
%X It has been demonstrated that vector-based representations of words trained on large text corpora encode linguistic regularities that may be exploited via the use of vector space arithmetic. This capability has been extensively explored and is generally measured via tasks which involve the automated completion of linguistic proportional analogies. The question remains, however, as to what extent it is possible to induce relations from word embeddings in a principled and systematic way, without the provision of exemplars or seed terms. In this paper we propose an extensible and efficient framework for inducing relations via the use of constraint satisfaction. The method is efficient, unsupervised and can be customized in various ways. We provide both quantitative and qualitative analysis of the results.
%U https://aclanthology.org/U18-1004
%P 34-43

Markdown (Informal)

[Unsupervised Mining of Analogical Frames by Constraint Satisfaction](https://aclanthology.org/U18-1004) (De Vine et al., ALTA 2018)

• Unsupervised Mining of Analogical Frames by Constraint Satisfaction (De Vine et al., ALTA 2018)

ACL

• Lance De Vine, Shlomo Geva, and Peter Bruza. 2018. Unsupervised Mining of Analogical Frames by Constraint Satisfaction. In Proceedings of the Australasian Language Technology Association Workshop 2018, pages 34–43, Dunedin, New Zealand.