EDTC: A Corpus for Discourse-Level Topic Chain Parsing
Longyin Zhang, Xin Tan, Fang Kong, Guodong Zhou
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.
BibTeX
@inproceedings{zhang-etal-2021-edtc-corpus,
title = "{EDTC}: A Corpus for Discourse-Level Topic Chain Parsing",
author = "Zhang, Longyin and
Tan, Xin and
Kong, Fang and
Zhou, Guodong",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
month = nov,
year = "2021",
address = "Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.findings-emnlp.113/",
doi = "10.18653/v1/2021.findings-emnlp.113",
pages = "1304--1312"
}