Abstract
Distributed representations of words encode lexical semantic information, but what type of information is encoded and how? Focusing on the skip-gram with negative-sampling method, we found that the squared norm of static word embedding encodes the information gain conveyed by the word; the information gain is defined by the Kullback-Leibler divergence of the co-occurrence distribution of the word to the unigram distribution. Our findings are explained by the theoretical framework of the exponential family of probability distributions and confirmed through precise experiments that remove spurious correlations arising from word frequency. This theory also extends to contextualized word embeddings in language models or any neural networks with the softmax output layer. We also demonstrate that both the KL divergence and the squared norm of embedding provide a useful metric of the informativeness of a word in tasks such as keyword extraction, proper-noun discrimination, and hypernym discrimination.- Anthology ID:
- 2023.emnlp-main.131
- Volume:
- Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Houda Bouamor, Juan Pino, Kalika Bali
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2108–2130
- Language:
- URL:
- https://aclanthology.org/2023.emnlp-main.131
- DOI:
- 10.18653/v1/2023.emnlp-main.131
- Cite (ACL):
- Momose Oyama, Sho Yokoi, and Hidetoshi Shimodaira. 2023. Norm of Word Embedding Encodes Information Gain. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 2108–2130, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- Norm of Word Embedding Encodes Information Gain (Oyama et al., EMNLP 2023)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/2023.emnlp-main.131.pdf