Yuefeng Li
over the years, businesses have collected very large and complex big data collections, and it has become increasingly difficult to process these big data using the tradition techniques. There is a big challenging issue since the majority of big data is unlabelled in unstructured (information that is not pre-defined) manner. Recently, AI (Artificial Intelligence) based techniques have been used to solve this big issue, e.g., understanding a firm’s reputation using on-line customer reviews, or retrieving of training samples from unlabelled tweets and so on. This talk discusses how AI techniques contribute to text classification and document summarization in the case of only obtaining limited user feedback information for relevance. It firstly discusses the principle of a new classification methodology “a three-way decision based binary classification” to understand the hard issue for dealing with the uncertain boundary between the positive class and negative class. It also extended the application of three-way decisions for text classification to document summarization and sentiment analysis. This talk will presents some new experimental results on several popular data collections, such as RCV1, Reuters-21578, Tweets2011 and Tweets2013, DUC 2006 and 2007, and Amazon review data collections. It also discusses many advanced techniques for obtain more knowledge from big data about the relevance in order to help people to create effective machine learning systems for processing big data, and several open issues regarding to AI-based data analysis for text, Web and media data.
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