NaCTeM paper on descriptive clustering at EACL 2017


We are pleased to announce that a paper describing our novel work on descriptive clustering will be presented at EACL 2017, to be held in Valencia, Spain, from 5th - 7th April, 2017.

Sato, M., Brockmeier, A. J., Kontonatsios, G., Mu, T., Goulermas, J. Y, Tsujii, J. and Ananiadou, S. (2017). Distributed Document and Phrase Co-embeddings for Descriptive Clustering. In Proceedings of EACL 2017, pp. 991-1001


Descriptive document clustering aims to automatically discover groups of semantically related documents and to assign a meaningful label to characterise the content of each cluster. In this paper, we present a descriptive clustering approach that employs a distributed representation model, namely the paragraph vector model, to capture semantic similarities between documents and phrases. The proposed method uses a joint representation of phrases and documents (i.e., a coembedding) to automatically select a descriptive phrase that best represents each document cluster. We evaluate our method by comparing its performance to an existing state-of-the-art descriptive clustering method that also uses co-embedding but relies on a bag-of-words representation. Results obtained on benchmark datasets demonstrate that the paragraph vector-based method obtains superior performance over the existing approach in both identifying clusters and assigning appropriate descriptive labels to them.

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