An improved system for sentence-level novelty detection in textual streams

Fu, Xinyu and Ch'ng, Eugene and Aickelin, Uwe (2015) An improved system for sentence-level novelty detection in textual streams. In: 3rd International Conference on Smart Sustainable City and Big Data (ICSSC), 27-28 July 2015, Shanghai, China.

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Novelty detection in news events has long been a difficult problem. A number of models performed well on specific data streams but certain issues are far from being solved, particularly in large data streams from the WWW where unpredictability of new terms requires adaptation in the vector space model. We present a novel event detection system based on the Incremental Term Frequency-Inverse Document Frequency (TF-IDF) weighting incorporated with Locality Sensitive Hashing (LSH). Our system could efficiently and effectively adapt to the changes within the data streams of any new terms with continual updates to the vector space model. Regarding miss probability, our proposed novelty detection framework outperforms a recognised baseline system by approximately 16% when evaluating a benchmark dataset from Google News.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Due to be published by IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Keywords: first story detection, novelty detection, Locality Sensitive Hashing, text mining
Schools/Departments: University of Nottingham UK Campus > Faculty of Science > School of Computer Science
Depositing User: Aickelin, Professor Uwe
Date Deposited: 14 Oct 2015 08:56
Last Modified: 14 Sep 2016 01:22

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