Multivariate Gaussian Document Representation from Word Embeddings for Text Categorization
Giannis Nikolentzos, Polykarpos Meladianos, François Rousseau, Yannis Stavrakas, Michalis Vazirgiannis
Abstract
Recently, there has been a lot of activity in learning distributed representations of words in vector spaces. Although there are models capable of learning high-quality distributed representations of words, how to generate vector representations of the same quality for phrases or documents still remains a challenge. In this paper, we propose to model each document as a multivariate Gaussian distribution based on the distributed representations of its words. We then measure the similarity between two documents based on the similarity of their distributions. Experiments on eight standard text categorization datasets demonstrate the effectiveness of the proposed approach in comparison with state-of-the-art methods.- Anthology ID:
- E17-2072
- Volume:
- Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers
- Month:
- April
- Year:
- 2017
- Address:
- Valencia, Spain
- Editors:
- Mirella Lapata, Phil Blunsom, Alexander Koller
- Venue:
- EACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 450–455
- Language:
- URL:
- https://aclanthology.org/E17-2072
- DOI:
- Cite (ACL):
- Giannis Nikolentzos, Polykarpos Meladianos, François Rousseau, Yannis Stavrakas, and Michalis Vazirgiannis. 2017. Multivariate Gaussian Document Representation from Word Embeddings for Text Categorization. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers, pages 450–455, Valencia, Spain. Association for Computational Linguistics.
- Cite (Informal):
- Multivariate Gaussian Document Representation from Word Embeddings for Text Categorization (Nikolentzos et al., EACL 2017)
- PDF:
- https://preview.aclanthology.org/ml4al-ingestion/E17-2072.pdf