Analysis of Clustering Algorithms for Web-Based Search
PAKM '02 Proceedings of the 4th International Conference on Practical Aspects of Knowledge Management
An approach to clustering abstracts
NLDB'05 Proceedings of the 10th international conference on Natural Language Processing and Information Systems
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This paper focuses on the use of sense clusters for classification and clustering of very short texts such as conference abstracts. Common keyword-based techniques are effective for very short documents only when the data pertain to different domains. In the case of conference abstracts, all the documents are from a narrow domain (i.e., share a similar terminology), that increases the difficulty of the task. Sense clusters are extracted from abstracts, exploiting the WordNet relationships existing between words in the same text. Experiments were carried out both for the categorization task, using Bernoulli mixtures for binary data, and the clustering task, by means of Stein’s MajorClust method.