Keyword Extraction and Clustering for Document Recommendation in Conversations
Abstract— Keyword Extraction and Clustering for Document Recommendation in Conversations. The problem of keyword extraction from conversations, with the goal of using these keywords to retrieve, for each short conversation fragment, a small number of potentially relevant documents, which can be recommended to participants. However, even a short fragment contains a variety of words,< Final Year Project 2016 > which are potentially related to several topics; moreover, using an automatic speech recognition ASR system introduces errors among them. Therefore, it is difﬁcult to infer precisely the information needs of the conversation participants. We ﬁrst propose an algorithm to extract keywords from the output of an ASR system (or a manual transcript for testing), which makes use of topic modeling techniques and of a submodular reward func-tion which favors diversity in the keyword set, to match the potential diversity of topics and reduce ASR noise.
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