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
- Editors:
- Martha Palmer, Rebecca Hwa, Sebastian Riedel
- Venue:
- EMNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2748–2757
- Language:
- URL:
- https://aclanthology.org/D17-1291
- DOI:
- 10.18653/v1/D17-1291
- Cite (ACL):
- Honglei Zhuang, Chi Wang, Fangbo Tao, Lance Kaplan, and Jiawei Han. 2017. Identifying Semantically Deviating Outlier Documents. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 2748–2757, Copenhagen, Denmark. Association for Computational Linguistics.
- Cite (Informal):
- Identifying Semantically Deviating Outlier Documents (Zhuang et al., EMNLP 2017)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-5/D17-1291.pdf