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Document Clustering as an approach to template extraction
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Orientador(es)
Resumo(s)
A great part of customer support is done via the exchange of emails. As the number of emails exchanged daily is constantly increasing, companies need to find approaches to ensure its efficiency. One common strategy is the usage of template emails as an answer. These answers templates are usually found by a human agent through the repetitive usage of the same answer. In this work, we use a clustering approach to find these answer templates. Several clustering algorithms are researched in this work, with a focus on the k-means methodology, as well as other clustering components such as similarity measures and pre-processing steps. As we are dealing with text data, several text representation methods are also compared. Due to the peculiarity of the provided data, we are able to design methodologies to ensure the feasibility of this task and develop strategies to extract the answer templates from the clustering results.
Descrição
Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business Intelligence
Palavras-chave
Document Clustering Similarity Measures Text Representation Template Natural Language Processing