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

Ali-Reza Ebadat (1) , Vincent Claveau (1) , Pascale Sébillot (1)

1 TEXMEX - Multimedia content-based indexing

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.

Mots clés: vector representation; bag-of-bag-of-words; bag-of-vectors; similarity; clustering

Domaines: Traitement du texte et du document

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. ⟨hal-00753912⟩