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Novelty detection in news events has long been a difficult problem. A numberof models performed well on specific data streams but certain issues are farfrom being solved, particularly in large data streams from the WWW whereunpredictability of new terms requires adaptation in the vector space model. Wepresent a novel event detection system based on the Incremental TermFrequency-Inverse Document Frequency (TF-IDF) weighting incorporated withLocality Sensitive Hashing (LSH). Our system could efficiently and effectivelyadapt to the changes within the data streams of any new terms with continualupdates to the vector space model. Regarding miss probability, our proposednovelty detection framework outperforms a recognised baseline system byapproximately 16% when evaluating a benchmark dataset from Google News.

An Improved System for Sentence-level Novelty Detection in Textual Streams