Abstract
Word sense embeddings represent a word sense as a low-dimensional numeric vector. While this representation is potentially useful for NLP applications, its interpretability is inherently limited. We propose a simple technique that improves interpretability of sense vectors by mapping them to synsets of a lexical resource. Our experiments with AdaGram sense embeddings and BabelNet synsets show that it is possible to retrieve synsets that correspond to automatically learned sense vectors with Precision of 0.87, Recall of 0.42 and AUC of 0.78.- Anthology ID:
- L16-1421
- Volume:
- Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)
- Month:
- May
- Year:
- 2016
- Address:
- Portorož, Slovenia
- Editors:
- Nicoletta Calzolari, Khalid Choukri, Thierry Declerck, Sara Goggi, Marko Grobelnik, Bente Maegaard, Joseph Mariani, Helene Mazo, Asuncion Moreno, Jan Odijk, Stelios Piperidis
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association (ELRA)
- Note:
- Pages:
- 2649–2655
- Language:
- URL:
- https://aclanthology.org/L16-1421
- DOI:
- Cite (ACL):
- Alexander Panchenko. 2016. Best of Both Worlds: Making Word Sense Embeddings Interpretable. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16), pages 2649–2655, Portorož, Slovenia. European Language Resources Association (ELRA).
- Cite (Informal):
- Best of Both Worlds: Making Word Sense Embeddings Interpretable (Panchenko, LREC 2016)
- PDF:
- https://preview.aclanthology.org/emnlp22-frontmatter/L16-1421.pdf