UNAM at SemEval-2018 Task 10: Unsupervised Semantic Discriminative Attribute Identification in Neural Word Embedding Cones

Ignacio Arroyo-Fernández, Ivan Meza, Carlos-Francisco Méndez-Cruz


Abstract
In this paper we report an unsupervised method aimed to identify whether an attribute is discriminative for two words (which are treated as concepts, in our particular case). To this end, we use geometrically inspired vector operations underlying unsupervised decision functions. These decision functions operate on state-of-the-art neural word embeddings of the attribute and the concepts. The main idea can be described as follows: if attribute q discriminates concept a from concept b, then q is excluded from the feature set shared by these two concepts: the intersection. That is, the membership q∈ (a∩ b) does not hold. As a,b,q are represented with neural word embeddings, we tested vector operations allowing us to measure membership, i.e. fuzzy set operations (t-norm, for fuzzy intersection, and t-conorm, for fuzzy union) and the similarity between q and the convex cone described by a and b.
Anthology ID:
S18-1161
Volume:
Proceedings of The 12th International Workshop on Semantic Evaluation
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Venue:
*SEMEVAL
SIGs:
SIGLEX | SIGSEM
Publisher:
Association for Computational Linguistics
Note:
Pages:
977–984
Language:
URL:
https://www.aclweb.org/anthology/S18-1161
DOI:
10.18653/v1/S18-1161
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/S18-1161.pdf