Abstract
POS tagging (i.e. part-of-speech tagging) is an important component of syntactic parsing in the field of natural language processing. While CRF (i.e. conditional random field) is a class of statistical modelling method often applied in pattern recognition and machine learning, where it is used for structured prediction. As POS tagging can be considered as a structured prediction task to some extent, so in this paper, we proposed to utilize the inherent advantages of CRF, and apply it to POS tagging task to get more accurate. The subsequent experiments are introduced to validate our proposed method.
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Zhang, J., Zhang, Y. (2016). Accurate Part-of-Speech Tagging via Conditional Random Field. In: Hsu, CH., Wang, S., Zhou, A., Shawkat, A. (eds) Internet of Vehicles – Technologies and Services. IOV 2016. Lecture Notes in Computer Science(), vol 10036. Springer, Cham. https://doi.org/10.1007/978-3-319-51969-2_18
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DOI: https://doi.org/10.1007/978-3-319-51969-2_18
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