An improved system for sentence-level novelty detection in textual streamsTools Fu, Xinyu, Ch'ng, Eugene and Aickelin, Uwe (2016) 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. Full text not available from this repository.
Official URL: http://ieeexplore.ieee.org/document/7446433/
AbstractNovelty 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.
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