Supervised Topic Segmentation of Email Conversations

Authors

  • Shafiq Joty University of British Columbia
  • Giuseppe Carenini University of British Columbia
  • Gabriel Murray University of British Columbia
  • Raymond Ng University of British Columbia

Abstract

We propose a graph-theoretic supervised topic segmentation model for email conversations which combines (i) lexical knowledge, (ii) conversational features, and (iii) topic features. We compare our results with the existing unsupervised models (i.e., LCSeg and LDA), and with their two extensions for email conversations (i.e., LCSeg+FQG and LDA+FQG) that not only use lexical information but also exploit finer conversation structure. Empirical evaluation shows that our supervised model is the best performer and achieves highest accuracy by combining the three different knowledge sources, where knowledge about the conversation has proved to be the most important indicator for segmenting emails.

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Published

2021-08-03

How to Cite

Joty, S., Carenini, G., Murray, G., & Ng, R. (2021). Supervised Topic Segmentation of Email Conversations. Proceedings of the International AAAI Conference on Web and Social Media, 5(1), 530-533. Retrieved from https://ojs.aaai.org/index.php/ICWSM/article/view/14198