Building Language Models for Text with Named Entities
Md Rizwan Parvez, Saikat Chakraborty, Baishakhi Ray, Kai-Wei Chang
Abstract
Text in many domains involves a significant amount of named entities. Predicting the entity names is often challenging for a language model as they appear less frequent on the training corpus. In this paper, we propose a novel and effective approach to building a language model which can learn the entity names by leveraging their entity type information. We also introduce two benchmark datasets based on recipes and Java programming codes, on which we evaluate the proposed model. Experimental results show that our model achieves 52.2% better perplexity in recipe generation and 22.06% on code generation than state-of-the-art language models.- Anthology ID:
- P18-1221
- Volume:
- Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- July
- Year:
- 2018
- Address:
- Melbourne, Australia
- Editors:
- Iryna Gurevych, Yusuke Miyao
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2373–2383
- Language:
- URL:
- https://aclanthology.org/P18-1221
- DOI:
- 10.18653/v1/P18-1221
- Cite (ACL):
- Md Rizwan Parvez, Saikat Chakraborty, Baishakhi Ray, and Kai-Wei Chang. 2018. Building Language Models for Text with Named Entities. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2373–2383, Melbourne, Australia. Association for Computational Linguistics.
- Cite (Informal):
- Building Language Models for Text with Named Entities (Parvez et al., ACL 2018)
- PDF:
- https://preview.aclanthology.org/naacl24-info/P18-1221.pdf
- Code
- uclanlp/NamedEntityLanguageModel + additional community code