Automatic Construction Labeled Clusters by Teffera Henock (8 results)

Condition: New. pp. 64.

Taschenbuch. Condition: Neu. Automatic construction of labeled clusters of named entities for IR | Thesis for European Master's in Language and Communication Technology | Henock Tilahun Teffera | Taschenbuch | 64 S. | Englisch | 2011 | LAP LAMBERT Academic Publishing | EAN 9783844334722 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.…

Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 64 pp. Englisch.…

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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.…

Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Teffera Henock TilahunI have studied computer science at Mekelle University, Ethiopia. Immediately after graduation, I got teaching assistant job in the same department where I studied. I have worked in the University for two years.…

Condition: New. Print on Demand pp. 64 2:B&W 6 x 9 in or 229 x 152 mm Perfect Bound on Creme w/Gloss Lam.

Condition: New. PRINT ON DEMAND pp. 64.

Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 64 pp. Englisch.…