Today more and more information on the Web makes it difficult to get domain-specific information due to the huge amount of data sources and the keywords that have few features. Anchor texts, which contain a few features of a specific topic, play an important role in domain-specific information retrieval, especially in Web page classification. However, the features contained in anchor texts are not informative enough. This paper presents a novel incremental method for Web page classification enhanced by link-contexts and clustering. Directly applying the vector of anchor text to a classifier might not get a good result because of the limited amount of features. Link-context is used first to obtain the contextual information of the anchor text. Then, a hierarchical clustering method is introduced to cluster feature vectors and content unit, which increases the length of a feature vector belonging to one specific class. Finally, incremental SVM is proposed to get the final classifier and increase the accuracy and efficiency of a classifier. Experimental results show that the performance of our proposed method outperforms the conventional topical Web crawler in Harvest rate and Target recall.
Analogy-based Matching Model for Domain-specific Information Retrieval
