Abstract
Discourse analysis has long been known to be fundamental in natural language processing. In this research, we present our insight on discourse-level topic chain (DTC) parsing which aims at discovering new topics and investigating how these topics evolve over time within an article. To address the lack of data, we contribute a new discourse corpus with DTC-style dependency graphs annotated upon news articles. In particular, we ensure the high reliability of the corpus by utilizing a two-step annotation strategy to build the data and filtering out the annotations with low confidence scores. Based on the annotated corpus, we introduce a simple yet robust system for automatic discourse-level topic chain parsing.- Anthology ID:
- 2021.findings-emnlp.113
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2021
- Month:
- November
- Year:
- 2021
- Address:
- Punta Cana, Dominican Republic
- Editors:
- Marie-Francine Moens, Xuanjing Huang, Lucia Specia, Scott Wen-tau Yih
- Venue:
- Findings
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1304–1312
- Language:
- URL:
- https://aclanthology.org/2021.findings-emnlp.113
- DOI:
- 10.18653/v1/2021.findings-emnlp.113
- Cite (ACL):
- Longyin Zhang, Xin Tan, Fang Kong, and Guodong Zhou. 2021. EDTC: A Corpus for Discourse-Level Topic Chain Parsing. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 1304–1312, Punta Cana, Dominican Republic. Association for Computational Linguistics.
- Cite (Informal):
- EDTC: A Corpus for Discourse-Level Topic Chain Parsing (Zhang et al., Findings 2021)
- PDF:
- https://preview.aclanthology.org/dois-2013-emnlp/2021.findings-emnlp.113.pdf
- Code
- nlp-discourse-soochowu/dtcp