Topic tracking techniques for natural language processing

Kaur, Kamaldeep

doi:10.1145/2007052.2007066

Cited by 1 publication

(3 citation statements)

References 18 publications

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“…Traditional TDT methods use serial monitor incoming news stories, namely, to compute the similarity of topic and story one by one. Actually, there are numerous news stories arriving in a short time, and stories having the same high‐frequency features in this short period usually belong to the same topic.…”

Section: Resultsmentioning

confidence: 99%

“…In order to distinguish the concepts in linguistic, related documents give definitions for some concepts: Definition Topic . A topic is defined to be a seminal event or activity, along with all directly related events and activities. Definition Topic Detection .…”

Section: Research Foundationmentioning

confidence: 99%

“…In order to distinguish the concepts in linguistic, related documents [15][16][17][18] give definitions for some concepts:…”

Section: Related Definitionmentioning

confidence: 99%

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Using clustering and dynamic mutual information for topic feature selection

Shu

Zhu³

2014

J Soc Info Display

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-A good feature selection method should take into account both category information and high-frequency information to select useful features that can effectively display the information of a target. Because basic mutual information (BMI) prefers low-frequency features and ignores high-frequency features, clustering mutual information is proposed, which is based on clustering and makes effective high-frequency features become unique, better integrating category information and useful highfrequency information. Time is an important factor in topic detection and tracking (TDT). In order to improve the performance of TDT, time difference is integrated into clustering mutual information to dynamically adjust the mutual information, and then another algorithm called the dynamic clustering mutual information (DCMI) is given. In order to obtain the optimal subsets to display topics information, an objective function is proposed, which is based on the idea that a good feature subset should have the smallest distance within-class and the largest distance across-class. Experiments on TDT4 corpora using this objective function are performed; then, comparing the performances of BMI, DCMI, and the only existed topic feature selection algorithm Incremental Term Frequency-Inverted Document Frequency (ITF-IDF), these performance information will be displayed by four figures. Computation time of DCMI is previously lower than BMI and ITF-IDF. The optimal normalized-detection performance (C det ) norm of DCMI is decreased by 0.3044 and 0.0970 compared with those of BMI and ITF-IDF, respectively.

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Section: Resultsmentioning

confidence: 99%