With the popularity of social media platforms such as Facebook and Twitter, the amount of useful data in these sources is rapidly increasing, making them promising places for information acquisition. This research aims at the customized organization of a social media corpus using focused topic hierarchy. It organizes the contents into different structures to meet with users' different information needs (e.g., "iPhone 5 problem" or "iPhone 5 camera"). To this end, we introduce a novel function to measure the likelihood of a topic hierarchy, by which the users' information need can be incorporated into the process of topic hierarchy construction. Using the structure information within the generated topic hierarchy, we then develop a probability based model to identify the representative contents for topics to assist users in document retrieval on the hierarchy. Experimental results on real world data illustrate the effectiveness of our method and its superiority over state-of-the-art methods for both information organization and retrieval tasks.

Thanks to the idea of social collaboration, Wikipedia has accumulated vast amount of semi-structured knowledge in which the link structure reflects human's cognition on semantic relationship to some extent. In this paper, we proposed a novel method RCRank to jointly compute concept-concept relatedness and concept-category relatedness base on the assumption that information carried in concept-concept links and concept-category links can mutually reinforce each other. Different from previous work, RCRank can not only find semantically related concepts but also interpret their relations by categories. Experimental results on concept recommendation and relation interpretation show that our method substantially outperforms classical methods.

We provide three key missing pieces of a general theory of information distance [3, 23, 24]. We take bold steps in formulating a revised theory to avoid some pitfalls in practical applications. The new theory is then used to construct a question answering system. Extensive experiments are conducted to justify the new theory.

This paper introduces a Chinese spoken dialog system providing services for blind people through which. they can use computers. A description of the architecture of the dialog system is presented briefly and the way in which each component works is also explained. The key factor of such a dialog system is extraction of the intention of a user's utterance so as to make an appropriate response. To achieve this, a case grammar formalism was applied for semantic description and a robust spoken language parsing method based on case-frames was adopted to obtain the semantic interpretation of the input. It shows that this parsing method can tolerate errors of speech recognition and grammatical deviation of spoken language to some extent.