Year: 2017
Type: conference-paper
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.
DOI: 10.18653/v1/d17-1291
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Awards: G1346049954, III: Small: Collaborative Research: Conflicts to Harmony: Integrating Massive Data by Trustworthiness Estimation and Truth Discovery, III: Small: Multi-Dimensional Structuring, Summarizing and Mining of Social Media Data, G3346663007, G5226638049, G5259331294, G6015371281, G7561134949, III: Medium: Collaborative Research: StructNet: Constructing and Mining Structure-Rich Information Networks for Scientific Research, G9000120328, G948678646
OpenAlex ID: W2757911951