Identifying Semantically Deviating Outlier Documents

Honglei Zhuang, Chi Wang, Fangbo Tao, Lance Kaplan, Jiawei Han


Abstract
A document outlier is a document that substantially deviates in semantics from the majority ones in a corpus. Automatic identification of document outliers can be valuable in many applications, such as screening health records for medical mistakes. In this paper, we study the problem of mining semantically deviating document outliers in a given corpus. We develop a generative model to identify frequent and characteristic semantic regions in the word embedding space to represent the given corpus, and a robust outlierness measure which is resistant to noisy content in documents. Experiments conducted on two real-world textual data sets show that our method can achieve an up to 135% improvement over baselines in terms of recall at top-1% of the outlier ranking.
Anthology ID:
D17-1291
Volume:
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
Month:
September
Year:
2017
Address:
Copenhagen, Denmark
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
2748–2757
Language:
URL:
https://www.aclweb.org/anthology/D17-1291
DOI:
10.18653/v1/D17-1291
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PDF:
http://aclanthology.lst.uni-saarland.de/D17-1291.pdf