Tek Yong Lim

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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)
Most efforts at automatically creating multilingual lexicons require input lexical resources with rich content (e.g. semantic networks, domain codes, semantic categories) or large corpora. Such material is often unavailable and difficult to construct for under-resourced languages. In some cases, particularly for some ethnic languages, even unannotated(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)
Current approaches for word sense dis-ambiguation and translation selection typically require lexical resources or large bilingual corpora with rich information fields and annotations, which are often infeasible for under-resourced languages. We extract translation context knowledge from a bilingual comparable corpora of a richer-resourced language pair,(More)