Context-aware conversation thread detection in multi-party chat

23Citations
Citations of this article
87Readers
Mendeley users who have this article in their library.

Abstract

In multi-party chat, it is common for multiple conversations to occur concurrently, leading to intermingled conversation threads in chat logs. In this work, we propose a novel Context-Aware Thread Detection (CATD) model that automatically disentangles these conversation threads. We evaluate our model on three real-world datasets and demonstrate an overall improvement in thread detection accuracy over state-of-the-art benchmarks.

Cite

CITATION STYLE

APA

Tan, M., Wang, D., Gao, Y., Wang, H., Potdar, S., Guo, X., … Yu, M. (2019). Context-aware conversation thread detection in multi-party chat. In EMNLP-IJCNLP 2019 - 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing, Proceedings of the Conference (pp. 6456–6461). Association for Computational Linguistics. https://doi.org/10.18653/v1/d19-1682

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free