Keyword extraction from emails

被引:17
|
作者
Lahiri, S. [1 ]
Mihalcea, R. [1 ]
Lai, P. -H. [2 ]
机构
[1] Univ Michigan, Ann Arbor, MI 48109 USA
[2] Samsung Res Amer, Richardson, TX 75082 USA
基金
美国国家科学基金会;
关键词
Electronic mail;
D O I
10.1017/S1351324916000231
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Emails constitute an important genre of online communication. Many of us are often faced with the daunting task of sifting through increasingly large amounts of emails on a daily basis. Keywords extracted from emails can help us combat such information overload by allowing a systematic exploration of the topics contained in emails. Existing literature on keyword extraction has not covered the email genre, and no human-annotated gold standard datasets are currently available. In this paper, we introduce a new dataset for keyword extraction from emails, and evaluate supervised and unsupervised methods for keyword extraction from emails. The results obtained with our supervised keyword extraction system (38.99% F-score) improve over the results obtained with the best performing systems participating in the SemEval 2010 keyword extraction task.
引用
收藏
页码:295 / 317
页数:23
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