A new statistical strategy for pooling: ELI

Kocabaş, İ̇lker; Dinçer, Bekir Taner

doi:10.1016/j.ipl.2013.07.007

Cited by 1 publication

(1 citation statement)

References 17 publications

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“…It is not feasible to assess the relevance of the huge result given by a search engine instead relevance is assessed for only a subset of the documents. The most standard approach is pooling [32]. In this system only top k documents returned by the search engine is assessed for relevance.…”

Section: Dataset and Experimentationmentioning

confidence: 99%

Web Search Result Clustering using Heuristic Search and Latent Semantic Indexing

Alam¹,

Sadaf²

2012

IJCA

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Giving user a simple and well organized web search result has been a topic of active information Retrieval (IR) research. Irrespective of how small or ambiguous a query is, a user always wants the desired result on the first display of an IR system. Clustering of an IR system's result can render a way, which fulfills the user's actual information need. In this paper, an approach to cluster an IR system's result is presented. The approach is a combination of heuristics and k-means technique using cosine similarity. Our heuristic approach detects the initial value of k for creating initial centroids. This eliminates the problem of external specification of the value k, which may lead to unwanted result if wrongly specified. The centroids created in this way are more specific and meaningful in the context of web search result. Another advantage of the proposed method is the removal of the objective means function of k-means which makes clusters' sizes same. The end result of the proposed approach consists of different clusters of documents having different sizes.

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Section: Dataset and Experimentationmentioning

confidence: 99%