2019
IJCNLP
IJCNLP 2019
Context-Aware Conversation Thread Detection in Multi-Party Chat
Abstract
AbstractIn 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 four real-world datasets and demonstrate an overall im-provement in thread detection accuracy over state-of-the-art benchmarks.
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Keyword Pioneer
— thread detection
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Hot Topic Early Bird
— conversation analysis
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Security & Privacy, Speech & Audio
Authors
Ming Tan
,
Dakuo Wang
,
Yupeng Gao
,
Haoyu Wang
,
Saloni Potdar
,
Xiaoxiao Guo
,
Shiyu Chang
,
Mo Yu