Abstract
This paper presents a precision oriented example based approach for word sense disambiguation (WSD) for a reading assistant system for Japanese learners. Our WSD classifier chooses a sense associated with the most similar sentence in a dictionary only if the similarity is high enough, otherwise chooses no sense. We propose sentence similarity measures by exploiting collocations and syntactic dependency relations for a target word. The example based classifier is combined with a Robinson classifier to compensate recall. We further improve WSD performance by automatically acquiring bilingual sentences from a parallel corpus. According to the results of our experiments, the accuracy of automatically extracted sentences was 85%, while the proposed WSD method achieves 65% accuracy which is 7% higher than the baseline.
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Kathuria, P., Shirai, K. (2012). Word Sense Disambiguation Based on Example Sentences in Dictionary and Automatically Acquired from Parallel Corpus. In: Isahara, H., Kanzaki, K. (eds) Advances in Natural Language Processing. JapTAL 2012. Lecture Notes in Computer Science(), vol 7614. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33983-7_21
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DOI: https://doi.org/10.1007/978-3-642-33983-7_21
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