Semantic Clustering using Bag-of-Bag-of-Features

Abstract : Computing distances between textual representation is at the heart of many Natural Language Processing tasks. The standard approaches initially developed for Information Retrieval are then used; most often they rely on a bag-of-words (or bag-of-feature) description with a TF-IDF (or variants) weighting, a vectorial representation and classical similarity functions like cosine. In this paper, we are interested in such a task, namely the semantic clustering of entities extracted from a text. We argue that for this kind of tasks, more suited representations and similarity measures can be used. In particular, we explore the use of alternative representation for entities called Bag-Of-Vectors (or Bag-of-Bags-of-Features). In this new model, each entity is not defined as a unique vector but as a set of vectors, in which each vector is built based on the contextual features of one occurrence of the entity. In order to use Bag-Of-Vectors for clustering, we introduce new versions of classical similarity functions such as Cosine, Jaccard and Scalar Products. Experimentally, we show that the Bag-Of-Vectors representation always improve the clustering results compared to classical Bag-Of-Features representations.
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Conference papers
CORIA - COnférence en Recherche d'Information et Applications, Mar 2012, Bordeaux, France. pp.229-244, 2012


https://hal.archives-ouvertes.fr/hal-00753912
Contributor : Pascale Sébillot <>
Submitted on : Monday, November 19, 2012 - 7:35:35 PM
Last modification on : Tuesday, February 3, 2015 - 11:44:52 AM

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Ali-Reza Ebadat, Vincent Claveau, Pascale Sébillot. Semantic Clustering using Bag-of-Bag-of-Features. CORIA - COnférence en Recherche d'Information et Applications, Mar 2012, Bordeaux, France. pp.229-244, 2012. <hal-00753912>

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