Abstract
In this article, we propose to investigate a new problem consisting in turning a distributional thesaurus into dense word vectors. We propose more precisely a method for performing such task by associating graph embedding and distributed representation adaptation. We have applied and evaluated it for English nouns at a large scale about its ability to retrieve synonyms. In this context, we have also illustrated the interest of the developed method for three different tasks: the improvement of already existing word embeddings, the fusion of heterogeneous representations and the expansion of synsets.- Anthology ID:
- I17-1028
- Volume:
- Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
- Month:
- November
- Year:
- 2017
- Address:
- Taipei, Taiwan
- Editors:
- Greg Kondrak, Taro Watanabe
- Venue:
- IJCNLP
- SIG:
- Publisher:
- Asian Federation of Natural Language Processing
- Note:
- Pages:
- 273–283
- Language:
- URL:
- https://aclanthology.org/I17-1028
- DOI:
- Cite (ACL):
- Olivier Ferret. 2017. Turning Distributional Thesauri into Word Vectors for Synonym Extraction and Expansion. In Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 273–283, Taipei, Taiwan. Asian Federation of Natural Language Processing.
- Cite (Informal):
- Turning Distributional Thesauri into Word Vectors for Synonym Extraction and Expansion (Ferret, IJCNLP 2017)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/I17-1028.pdf