Abstract
This paper presents an accurate and highly efficient rule-based part-of-speech tagger for Bulgarian. All four stages – tokenization, dictionary application, unknown words guessing and contextual part-of-speech disambiguation – are implemented as a pipeline of a couple deterministic finite state bimachines and transducers. We present a description of the Bulgarian ambiguity classes and a detailed evaluation and error analysis of our tagger. The overall precision of the tagger is over 98.4% for full disambiguation and the processing speed is over 34K words/sec on a personal computer. The same methodology has been applied for English as well. The presented realization conforms to the specific demands of the semantic web.
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© 2004 Springer-Verlag Berlin Heidelberg
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Doychinova, V., Mihov, S. (2004). High Performance Part-of-Speech Tagging of Bulgarian. In: Bussler, C., Fensel, D. (eds) Artificial Intelligence: Methodology, Systems, and Applications. AIMSA 2004. Lecture Notes in Computer Science(), vol 3192. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30106-6_25
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DOI: https://doi.org/10.1007/978-3-540-30106-6_25
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