Abstract
This paper proposes a robust method for word sense disambiguation (WSD) of Japanese. Four classifiers were combined in order to improve recall and applicability: one used example sentences in a machine readable dictionary (MRD), one used grammatical information in an MRD, and two classifiers were obtained by supervised learning from a sense-tagged corpus. In other words, we combined several classifiers using heterogeneous language resources, an MRD and a word sense tagged corpus. According to our experimental results, the proposed method outperformed the best single classifier for recall and applicability.
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Shirai, K., Tamagaki, T. (2005). Word Sense Disambiguation Using Heterogeneous Language Resources. In: Su, KY., Tsujii, J., Lee, JH., Kwong, O.Y. (eds) Natural Language Processing – IJCNLP 2004. IJCNLP 2004. Lecture Notes in Computer Science(), vol 3248. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30211-7_40
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DOI: https://doi.org/10.1007/978-3-540-30211-7_40
Publisher Name: Springer, Berlin, Heidelberg
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