Saravadee Sae Tan

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Having broad coverage of search results returned by various search sources, combining and organizing these results in a meaningful way has become a common issue in the field of information retrieval. In this demo paper, we describe our meta search system, MICE, that is able to aggregate and classify search results based on user-customized categories.(More)
This paper addresses the problem of matching between highly heterogeneous structures. The problem is modeled as a classification task where training examples are used to learn the matching between structures. In our approach, training is performed using partially labeled data. We propose a Greedy Mapping approach to generate training examples from partially(More)
There is an increasing popularity of people posting their feelings on microblogging such as Twitter. Sentiment analysis on the tweets allows organizations to monitor public' feelings towards a product or brand. In this paper, we model sentiment analysis problem as a multi-classification approach that utilizes various feature types, including(More)
Structured retrieval aims at exploiting the structural information of documents when searching for documents. Structured retrieval makes use of both content and structure of documents to improve information retrieval. Therefore, the availability of semantic structure in the documents is an important factor for the success of structured retrieval. However,(More)
EXTENDED ABSTRACT The retrieval of structured resources using unstructured queries is challenging as we need to deal with the matching between entities of two different types. Consider an unstructured query, " publications of K.H. Gan in WI " , in a structured retrieval system. To match this query to structured resources, the system needs to transform it(More)