NDLI: A context-aware approach to detection of short irrelevant texts

Content Provider	IEEE Xplore Digital Library
Author	Sihong Xie Jing Wang Amin, M.S. Baoshi Yan Bhasin, A. Yu, C. Yu, P.S.
Copyright Year	2015
Description	Author affiliation: Dept. of Comput. Sci., Univ. of Illinois at Chicago, Chicago, IL, USA (Sihong Xie; Jing Wang; Yu, C.; Yu, P.S.) \|\| LinkedIn Corp., Mountain View, CA, USA (Amin, M.S.; Baoshi Yan; Bhasin, A.)
Abstract	This paper presents a simple and effective framework that can detect irrelevant short text contents following blogs and news articles, etc. in a context-aware and timely fashion. Nowadays, websites such as Linkedin.com and CNN.com allow their visitors to leave comments after articles, and spammers are exploiting this feature to post irrelevant contents. Visited by millions of readers per day, these websites have extremely high visibility, and irrelevant comments have a detrimental effect on the visiting traffic and revenue of these websites. Therefore, it is critical to eliminate these irrelevant comments as accurately and early as possible. Different from traditional text mining tasks, comments following news and blog articles are characterized by briefness and context-dependent semantics, making it difficult to measure semantic relevance. What's worse, there could be only a handful of comments soon after an article is posted, leading to a severe lack of information for semantics and relevance measurement. We propose to infer “context-aware semantics” to address the above challenges in a unified framework. Specifically, we construct contexts for comments using either blocks of surrounding comments, or comments collected via a principled transfer learning approach. The constructed contexts mitigate the sparseness and sharply define context-dependent semantics of comments, even at the early stage of commenting activities, allowing traditional dimension reduction methods to better capture the semantics of short texts in a context-aware way. We confirm the effectiveness of the proposed method on two real world datasets consisting of news and blog articles and comments, with a maximal improvement of 20% in Area Under Precision-Recall Curve.
Sponsorship	IEEE Comput. Intell. Soc.
Starting Page	1
Ending Page	10
File Size	984125
Page Count	10
File Format	PDF
ISBN	9781467382724
DOI	10.1109/DSAA.2015.7344831
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-10-19
Publisher Place	France
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Context Text mining LinkedIn Semantics Blogs Context modeling
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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