Automatic construction of labeled clusters of named entities for IR
Thesis for European Master's in Language and Communication Technology
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In this study we have tried to harvest labeled clusters of semantically similar named entities which can be used as a first step for web document clustering. We first collect ~44,000 named entities from a thesaurus which is constructed by Dekang Lin applying a word similarity measure based on their distributional pattern. Using their similarity metrics and CLUTO clustering software, we create 2000 semantically similar clusters of the named entities. Then we collect ~305,500 label-instance pairs from the 2007 English Wikipedia dump and implement a labeling algorithm presented by Benjamin Van Du...
In this study we have tried to harvest labeled clusters of semantically similar named entities which can be used as a first step for web document clustering. We first collect ~44,000 named entities from a thesaurus which is constructed by Dekang Lin applying a word similarity measure based on their distributional pattern. Using their similarity metrics and CLUTO clustering software, we create 2000 semantically similar clusters of the named entities. Then we collect ~305,500 label-instance pairs from the 2007 English Wikipedia dump and implement a labeling algorithm presented by Benjamin Van Durme and M.Pasça (2008) to assign a label to the clusters. This automatic lableing task is able to assign a label which describes the majority of the named entities in 924 of the clusters, which is 46.2% of the total clusters. Finally we evaluate both the clustering and labeling tasks taking 86 randomly selected clusters and on the bases of two native English speaker evaluators subjective judgment. According to these evaluators, the clustering task has a purity score of 0.7 and 55% of the labels are acceptable with different degree of accuracy.
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