Discovering word senses from text
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Clustering Using Feature Domain Similarity to Discover Word Senses for Adjectives
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AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Language Resources and Evaluation
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One of the difficulties in using Folksonomies in computational systems is tag ambiguity: tags with multiple meanings. This paper presents a novel method for building Folksonomy tag ontologies in which the nodes are disambiguated. Our method utilizes a clustering algorithm called DSCBC, which was originally developed in Natural Language Processing (NLP), to derive committees of tags, each of which corresponds to one meaning or domain. In this work, we use Wikipedia as the external knowledge source for the domains of the tags. Using the committees, an ambiguous tag is identified as one which belongs to more than one committee. Then we apply a hierarchical agglomerative clustering algorithm to build an ontology of tags. The nodes in the derived ontology are disambiguated in that an ambiguous tag appears in several nodes in the ontology, each of which corresponds to one meaning of the tag. We evaluate the derived ontology for its ontological density (how close similar tags are placed), and its usefulness in applications, in particular for a personalized tag retrieval task. The results showed marked improvements over other approaches.