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
- 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)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/N18-1103.pdf
- Code
- nmrksic/lear
- Data
- HyperLex