A search result clustering method using informatively named entities

Toda, Hiroyuki; Kataoka, Ryuho

doi:10.1145/1097047.1097063

Cited by 61 publications

(27 citation statements)

References 13 publications

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“…The proposed techniques include using hyperlinks [Wang and Kitsuregawa 2002], named entities [Toda and Kataoka 2005], external information available on the Internet [Gabrilovich 2006], and temporal attributes [Alonso and Gertz 2006].…”

Section: Future Trendsmentioning

confidence: 99%

A survey of Web clustering engines

et al. 2009

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Web clustering engines organize search results by topic, thus offering a complementary view to the flat-ranked list returned by conventional search engines. In this survey, we discuss the issues that must be addressed in the development of a Web clustering engine, including acquisition and preprocessing of search results, their clustering and visualization. Search results clustering, the core of the system, has specific requirements that cannot be addressed by classical clustering algorithms. We emphasize the role played by the quality of the cluster labels as opposed to optimizing only the clustering structure. We highlight the main characteristics of a number of existing Web clustering engines and also discuss how to evaluate their retrieval performance. Some directions for future research are finally presented.

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Section: Future Trendsmentioning

confidence: 99%

A survey of Web clustering engines

et al. 2009

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“…Several state-of-the-art approaches to improve the automatic generation of clusters have been proposed in the literature [20,2,16,6,14]. The LUPI (Learning Using Privileged Information) paradigm, proposed by Vapnik and Vashist [21] to incorporate privileged information in the classification task, was applied to the clustering task in [6] and [14].…”

Section: Related Workmentioning

confidence: 99%

“…In [20], Toda and Kataoka proposed a method for clustering the results from search engines by using named entities for building an index of results (a structured labeled list). The algorithm proposed by them consists of the following steps: 1) Find the search results, 2) List the named entities for the search results, 3) Select the labels of the list of named entities, 4) Arrange the labels for categories of named entities.…”

Section: Related Workmentioning

confidence: 99%

Named entities as privileged information for hierarchical text clustering

Sinoara

Sundermann

Marcacini

et al. 2014

Proceedings of the 18th International Database Engineering &Amp; Applications Symposium on - IDEAS '14

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Text clustering is a text mining task which is often used to aid the organization, knowledge extraction, and exploratory search of text collections. Nowadays, the automatic text clustering becomes essential as the volume and variety of digital text documents increase, either in social networks and the Web or inside organizations. This paper explores the use of named entities as privileged information in a hierarchical clustering process, so as to improve clusters quality and interpretation. We carried out an experimental evaluation on three text collections (one written in Portuguese and two written in English) and the results show that named entities can be applied as privileged information to power clustering solution in dynamic text collection scenarios.

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“…In the context of web document clustering [13,14], objects are replaced by documents and are grouped together based upon some measure like similarity of content or of hyperlinked structure. As discussed earlier, most of the search engines return a large and unmanageable list of documents containing the query keywords.…”

Section: Document Clusteringmentioning

confidence: 99%

A Novel Approach for Organizing Web Search Results using Ranking and Clustering

Duhan¹,

Sharma²

2010

IJCA

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World Wide Web is considered the most valuable place for Information Retrieval and Knowledge Discovery. While retrieving information through user queries, a search engine results in a large and unmanageable collection of documents. Web mining tools are used to classify, cluster and order the documents so that users can easily navigate through the search results and find the desired information content. A more efficient way to organize the documents can be a combination of clustering and ranking, where clustering can group the documents and ranking can be applied for ordering the pages within each cluster. Based on this approach, in this paper, a mechanism is being proposed that provides ordered results in the form of clusters in accordance with user"s query. An efficient page ranking method is also proposed that orders the results according to both the relevancy and the importance of documents. This approach helps user to restrict his search to some top documents in particular clusters of his interest.

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A search result clustering method using informatively named entities

Cited by 61 publications

References 13 publications

A survey of Web clustering engines

A survey of Web clustering engines

Named entities as privileged information for hierarchical text clustering

A Novel Approach for Organizing Web Search Results using Ranking and Clustering

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