Abstract
This study proposes a Neural Attentive Bag-of-Entities model, which is a neural network model that performs text classification using entities in a knowledge base. Entities provide unambiguous and relevant semantic signals that are beneficial for text classification. We combine simple high-recall entity detection based on a dictionary, to detect entities in a document, with a novel neural attention mechanism that enables the model to focus on a small number of unambiguous and relevant entities. We tested the effectiveness of our model using two standard text classification datasets (i.e., the 20 Newsgroups and R8 datasets) and a popular factoid question answering dataset based on a trivia quiz game. As a result, our model achieved state-of-the-art results on all datasets. The source code of the proposed model is available online at https://github.com/wikipedia2vec/wikipedia2vec.- Anthology ID:
- K19-1052
- Volume:
- Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)
- Month:
- November
- Year:
- 2019
- Address:
- Hong Kong, China
- Editors:
- Mohit Bansal, Aline Villavicencio
- Venue:
- CoNLL
- SIG:
- SIGNLL
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 563–573
- Language:
- URL:
- https://aclanthology.org/K19-1052
- DOI:
- 10.18653/v1/K19-1052
- Cite (ACL):
- Ikuya Yamada and Hiroyuki Shindo. 2019. Neural Attentive Bag-of-Entities Model for Text Classification. In Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL), pages 563–573, Hong Kong, China. Association for Computational Linguistics.
- Cite (Informal):
- Neural Attentive Bag-of-Entities Model for Text Classification (Yamada & Shindo, CoNLL 2019)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/K19-1052.pdf
- Code
- wikipedia2vec/wikipedia2vec + additional community code