Abstract
In this paper we analyse the importance of data generalisation and usage of local context in the problem of the Proper Name recognition. We present an extended set of features that provide generalised description of the data and encode linguistic information. To utilize the rich set of features we applied Conditional Random Fields (CRF) — a modern approach for sequence labelling. We present results of the evaluation on a single domain following the cross-validation scheme and cross-domain evaluation based on training and testing on different corpora. We show that the extended set of features improves the final results for CRF and also this approach outperforms Hidden Markov Models (HMM). On the single domain CRF obtained 92.53% of F-measure for 5 categories of proper names, and 67.72% and 72.62% of F-measure for other two corpora in cross-domain evaluation.
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Marcińczuk, M., Stanek, M., Piasecki, M., Musiał, A. (2012). Rich Set of Features for Proper Name Recognition in Polish Texts. In: Bouvry, P., Kłopotek, M.A., Leprévost, F., Marciniak, M., Mykowiecka, A., Rybiński, H. (eds) Security and Intelligent Information Systems. SIIS 2011. Lecture Notes in Computer Science, vol 7053. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25261-7_26
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DOI: https://doi.org/10.1007/978-3-642-25261-7_26
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