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Community Question Answering (CQA) websites provide a rapidly growing source of information in many areas. This rapid growth, while offering new opportunities, puts forward new challenges. In most CQA implementations there is little effort in directing new questions to the right group of experts. This means that experts are not provided with questions(More)
This paper presents a statistical model for discovering topical clusters of words in unstructured text. The model uses a hierarchical Bayesian structure and it is also able to identify segments of text which are topically coherent. The model is able to assign each segment to a particular topic and thus categorizes the corresponding document to potentially(More)
Increasingly large text datasets and the high dimension-ality associated with natural language create a great challenge in text mining. In this research, a systematic study is conducted, in which three different document representation methods for text are used, together with three Dimension Reduction Techniques (DRT), in the context of the text clustering(More)
Community Question Answering (CQA) services contain large archives of previously asked questions and their answers. We present a statistical topic model for modeling Question-Answering archives. The model explicitly captures relationships between questions and their answers by modeling topical dependencies. We show that the model achieves improved(More)
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