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Learning to cluster web search results
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Acquisition of categorized named entities for web search
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Efficient support vector classifiers for named entity recognition
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Introduction to the bio-entity recognition task at JNLPBA
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A Novel Method for Hierarchical Clustering of Search Results
WI-IATW '07 Proceedings of the 2007 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Workshops
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Topic structure mining using temporal co-occurrence
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A survey of Web clustering engines
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Clustering and exploring search results using timeline constructions
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Scalable clustering of news search results
Proceedings of the fourth ACM international conference on Web search and data mining
Topic structure mining for document sets using graph-based analysis
DEXA'06 Proceedings of the 17th international conference on Database and Expert Systems Applications
A hierarchical document clustering environment based on the induced bisecting k-means
FQAS'06 Proceedings of the 7th international conference on Flexible Query Answering Systems
Topic structure mining using pagerank without hyperlinks
ICADL'06 Proceedings of the 9th international conference on Asian Digital Libraries: achievements, Challenges and Opportunities
A web 2.0 approach for organizing search results using wikipedia
AIRS'11 Proceedings of the 7th Asia conference on Information Retrieval Technology
Fine-grained topic detection in news search results
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A Graph Analytical Approach for Topic Detection
ACM Transactions on Internet Technology (TOIT)
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Clustering the results of a search helps the user to overview the information returned. In this paper, we regard the clustering task as indexing the search results. Here, an index means a structured label list that can makes it easier for the user to comprehend the labels and search results. To realize this goal, we make three proposals. First is to use Named Entity Extraction for term extraction. Second is a new label selecting criterion based on importance in the search result and the relation between terms and search queries. The third is label categorization using category information of labels, which is generated by NE extraction. We implement a prototype system based on these proposals and find that it offers much higher performance than existing methods; we focus on news articles in this paper.