Unsupervised Mining of Analogical Frames by Constraint Satisfaction
Lance De Vine | Shlomo Geva | Peter Bruza
Proceedings of the Australasian Language Technology Association Workshop 2018
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.
Semantic Judgement of Medical Concepts: Combining Syntagmatic and Paradigmatic Information with the Tensor Encoding Model
Michael Symonds | Guido Zuccon | Bevan Koopman | Peter Bruza | Anthony Nguyen
Proceedings of the Australasian Language Technology Association Workshop 2012
Modelling Word Meaning using Efficient Tensor Representations
Mike Symonds | Peter Bruza | Laurianne Sitbon | Ian Turner
Proceedings of the 25th Pacific Asia Conference on Language, Information and Computation
- Lance De Vine 1
- Shlomo Geva 1
- Mike Symonds 1
- Laurianne Sitbon 1
- Ian Turner 1
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