@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",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T EDTC: A Corpus for Discourse-Level Topic Chain Parsing
%A Zhang, Longyin
%A Tan, Xin
%A Kong, Fang
%A Zhou, Guodong
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Findings of the Association for Computational Linguistics: EMNLP 2021
%D 2021
%8 November
%I Association for Computational Linguistics
%C Punta Cana, Dominican Republic
%F zhang-etal-2021-edtc-corpus
%X 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.
%R 10.18653/v1/2021.findings-emnlp.113
%U https://aclanthology.org/2021.findings-emnlp.113
%U https://doi.org/10.18653/v1/2021.findings-emnlp.113
%P 1304-1312
Markdown (Informal)
[EDTC: A Corpus for Discourse-Level Topic Chain Parsing](https://aclanthology.org/2021.findings-emnlp.113) (Zhang et al., Findings 2021)
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.