Email Classification Incorporating Social Networks and Thread Structure

Sakhar Alkhereyf, Owen Rambow


Abstract
Existing methods for different document classification tasks in the context of social networks typically only capture the semantics of texts, while ignoring the users who exchange the text and the network they form. However, some work has shown that incorporating the social network information in addition to information from language is effective for various NLP applications including sentiment analysis, inferring user attributes, and predicting inter-personal relations. In this paper, we present an empirical study of email classification into “Business” and “Personal” categories. We represent the email communication using various graph structures. As features, we use both the textual information from the email content and social network information from the communication graphs. We also model the thread structure for emails. We focus on detecting personal emails, and we evaluate our methods on two corpora, only one of which we train on. The experimental results reveal that incorporating social network information improves over the performance of an approach based on textual information only. The results also show that considering the thread structure of emails improves the performance further. Furthermore, our approach improves over a state-of-the-art baseline which uses node embeddings based on both lexical and social network information.
Anthology ID:
2020.lrec-1.167
Volume:
Proceedings of the Twelfth Language Resources and Evaluation Conference
Month:
May
Year:
2020
Address:
Marseille, France
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
1336–1345
Language:
English
URL:
https://aclanthology.org/2020.lrec-1.167
DOI:
Bibkey:
Cite (ACL):
Sakhar Alkhereyf and Owen Rambow. 2020. Email Classification Incorporating Social Networks and Thread Structure. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 1336–1345, Marseille, France. European Language Resources Association.
Cite (Informal):
Email Classification Incorporating Social Networks and Thread Structure (Alkhereyf & Rambow, LREC 2020)
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PDF:
https://aclanthology.org/2020.lrec-1.167.pdf