Specialising Word Vectors for Lexical Entailment

Ivan Vulić, Nikola Mrkšić


Abstract
We present LEAR (Lexical Entailment Attract-Repel), a novel post-processing method that transforms any input word vector space to emphasise the asymmetric relation of lexical entailment (LE), also known as the IS-A or hyponymy-hypernymy relation. By injecting external linguistic constraints (e.g., WordNet links) into the initial vector space, the LE specialisation procedure brings true hyponymy-hypernymy pairs closer together in the transformed Euclidean space. The proposed asymmetric distance measure adjusts the norms of word vectors to reflect the actual WordNet-style hierarchy of concepts. Simultaneously, a joint objective enforces semantic similarity using the symmetric cosine distance, yielding a vector space specialised for both lexical relations at once. LEAR specialisation achieves state-of-the-art performance in the tasks of hypernymy directionality, hypernymy detection, and graded lexical entailment, demonstrating the effectiveness and robustness of the proposed asymmetric specialisation model.
Anthology ID:
N18-1103
Volume:
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1134–1145
Language:
URL:
https://aclanthology.org/N18-1103
DOI:
10.18653/v1/N18-1103
Bibkey:
Cite (ACL):
Ivan Vulić and Nikola Mrkšić. 2018. Specialising Word Vectors for Lexical Entailment. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 1134–1145, New Orleans, Louisiana. Association for Computational Linguistics.
Cite (Informal):
Specialising Word Vectors for Lexical Entailment (Vulić & Mrkšić, NAACL 2018)
Copy Citation:
PDF:
https://aclanthology.org/N18-1103.pdf
Video:
 http://vimeo.com/282333968
Code
 nmrksic/lear
Data
HyperLex