[1]LIU Su-qin,CHAI Song.K-means dynamic web topic detection method based on named entities[J].CAAI Transactions on Intelligent Systems,2010,5(2):122-126.
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K-means dynamic web topic detection method based on named entities

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 5 Number of periods: 2010 2 Page number: 122-126 Column: 学术论文—自然语言处理与理解 Public date: 2010-04-25
Title:
K-means dynamic web topic detection method based on named entities
Author(s):
LIU Su-qin1;  CHAI Song1; 2
1.College of Computer & Communication Engineering，China University of Petroleum, Qingdao 266555, China;
2.Automation Workstation，Military District， Shandong Province, Ji’nan 250013, China
Keywords:
named entity;  web topics;  dynamic detection;  Kmeans clustering method;  selfsimilarity;  topic vector
CLC:
TP18
DOI:
-
Abstract:
Current text representation models are not suitable for web topic detection, and the traditional Kmeans clustering algorithm has some drawbacks. The authors developed a dynamic Kmeans detection algorithm for web topics on the basis of named entities. In the new method, the representation model of the traditional topic detection method was modified. The text was represented by a combination of named entities and text features. The weight of the named entity was described by its contribution to the representation. The number of clusters K in the Kmeans algorithm selfconverged by the use of an adaptive technique. The Kmeans algorithm was optimized, achieving a dynamic detection of web topics by using dynamic selection of K values. Experimental results indicated that the new method detects and distinguishes between similar topics effectively, thus significantly improving the performance of topic detection.
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