Title of article :
Experiments in term weighting for novelty mining
Author/Authors :
Tsai، نويسنده , , Flora S. and Kwee، نويسنده , , Agus T. and Tang، نويسنده ,
Issue Information :
روزنامه با شماره پیاپی سال 2011
Abstract :
Obtaining new information in a short time is becoming crucial in today’s economy. A lot of information both offline or online is easily acquired, exacerbating the problem of information overload. Novelty mining detects documents/sentences that contain novel or new information and presents those results directly to users (Tang, Tsai, & Chen, 2010). Many methods and algorithms for novelty mining have previously been studied, but none have compared and discussed the impact of term weighting on the evaluation measures. This paper performed experiments to recommend the best term weighting function for both document and sentence-level novelty mining.
Keywords :
novelty detection , Inverse document frequency , binary , Threshold , Term frequency , Term weighting , Novelty dataset , Novelty mining
Journal title :
Expert Systems with Applications
Journal title :
Expert Systems with Applications