Abstract
Word segmentation is an essential step in building natural language applications such as machine translation, text summarization, and cross-lingual information retrieval. For certain oriental languages where word boundary is not clearly defined, a recognition process can become very challenging. One of the serious problems is dealing with word ambiguity. In this paper, we investigate the use of Linear Support Vector Machines (LSVM) for word boundary disambiguation. We empirically show, in the Vietnamese case, that LSVM obtains a better result when comparing to the Trigram Language Model approach.
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Nguyen, D., Zhang, D. (2010). Text Disambiguation Using Support Vector Machine: An Initial Study. In: Zhang, BT., Orgun, M.A. (eds) PRICAI 2010: Trends in Artificial Intelligence. PRICAI 2010. Lecture Notes in Computer Science(), vol 6230. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15246-7_60
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DOI: https://doi.org/10.1007/978-3-642-15246-7_60
Publisher Name: Springer, Berlin, Heidelberg
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