Abstract
An unsupervised method for part-of-speech discovery is presented whose aim is to induce a system of word classes by looking at the distributional properties of words in raw text. Our assumption is that the word pair consisting of the left and right neighbors of a particular token is characteristic of the part of speech to be selected at this position. Based on this observation, we cluster all such word pairs according to the patterns of their middle words. This gives us centroid vectors that are useful for the induction of a system of word classes and for the correct classification of ambiguous words.
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References
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© 2007 Springer-Verlag Berlin Heidelberg
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Rapp, R. (2007). Part-of-Speech Discovery by Clustering Contextual Features. In: Decker, R., Lenz, H.J. (eds) Advances in Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-70981-7_72
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DOI: https://doi.org/10.1007/978-3-540-70981-7_72
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-70980-0
Online ISBN: 978-3-540-70981-7
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