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We present a novel approach for discovering word categories, sets of words sharing a significant aspect of distributional context. We determine symmetric similarity of word pair, lexical category is then created based on graph-partitioning method. We train our model on a corpus of child-directed speech from CHILDES and show that the model successful learns word categories. Furthermore, a number of...
This paper proposes an unsupervised method for generating informative tags for multi-party dialogue in an open domain. Our model first extracts keywords from text through a multi-weighting framework, which includes frequency weighting, sentence weighting, speaker weighting and position weighting. Then we get their bigrams through frequent pattern matching. In order to generate more flexible and socialized...
In this work, we apply Dirichlet Process Mixture Models (DPMMs) to a cognitive computational task in natural language processing (NLP): lexical category acquisition. The model takes a corpus of child-directed speech from CHILDES as input. We assess the performance using a new measure we proposed that meets three criteria: informativeness, diversity and purity. The quantitative and qualitative evaluation...
Web page clustering based on semantic or topic promises improved search and browsing on the web. Intuitively, tags from social bookmarking websites such as del.icio.us can be used as a complementary source to document thus improving clustering of web pages. In this paper, we present a novel model which employs topic model to associate annotated document with a distribution of topics, and then constructs...
Emotion recognition is becoming a more and more hot topic nowadays. Many researches on facial recognition and speech recognition have been done. In this paper, we develop a method to facilitate emotion recognition with electroencephalographic (EEG) signals. The main objective of our study is to reveal the relation between emotional responses and EEG data gathered through giving seven subjects word-imagination...
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