Abstract
We present a PP-attachment disambiguation method based on a gigantic volume of unambiguous examples extracted from raw corpus. The unambiguous examples are utilized to acquire precise lexical preferences for PP-attachment disambiguation. Attachment decisions are made by a machine learning method that optimizes the use of the lexical preferences. Our experiments indicate that the precise lexical preferences work effectively.
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Kawahara, D., Kurohashi, S. (2005). PP-Attachment Disambiguation Boosted by a Gigantic Volume of Unambiguous Examples. In: Dale, R., Wong, KF., Su, J., Kwong, O.Y. (eds) Natural Language Processing – IJCNLP 2005. IJCNLP 2005. Lecture Notes in Computer Science(), vol 3651. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11562214_17
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DOI: https://doi.org/10.1007/11562214_17
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