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Emotion plays a significant role in human communications in our daily life. With progress in human-machine interface technology, recent research has placed more emphasis on the recognition of emotion reaction. Comparing to some other ideal experimental settings, blog posts online would be respond more to real-world events. And a huge resource of text-based emotion can be found from the World Wide...
In this work, we use Hidden Markov Models (HMM), Conditional Random Field (CRF), Gaussian Mixture Models (GMM) and Mathematical Methods of Statistics (MMS) for Chinese and Japanese text summarization. The purpose of this work is to study the applicability of mentioned three trainable models for cross-language text summarization. For model training, we use several training features such as sentence...
This work proposes an approach to address the problem of inductive bias or model misfit incurred by the centroid classifier assumption to enhance the automatic text classification task. This approach is a trainable classifier, which takes into account tfidf as a text feature. The main idea of the proposed approach is to take advantage of the most similar training errors to the classification model...
This work proposes an approach to address the problem of improving content selection in automatic text summarization by using probabilistic neural network (PNN). This approach is a trainable summarizer, which takes into account several features, including sentence position, positive keyword, negative keyword, sentence centrality, sentence resemblance to the title, sentence inclusion of name entity,...
Question classification is very important for question answering. This paper presents our research work on automatic question classification through support vector machine approaches. Unlike the classification using only bag-of-word features, we exploit the domain knowledge and question-specific stop words in our model, and also present how to enrich bag-of-word approach by implementing feature attributes...
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