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Optimizing Sentence Boundary Detection for Croatian

  • Conference paper

Part of the Lecture Notes in Computer Science book series (LNAI,volume 7499)

Abstract

A number of natural language processing tasks depend on segmenting text into sentences. Tools that perform sentence boundary detection achieve excellent performance for some languages. We have tried to train a few publicly available language independent tools to perform sentence boundary detection for Croatian. The initial results show that off-the-shelf methods used for English do not work particularly well for Croatian. After performing error analysis, we propose additional features that help in resolving some of the most common boundary detection errors. We use unsupervised methods on a large Croatian corpus to collect likely sentence starters, abbreviations, and honorifics. In addition to some commonly used features, we use these lists of words as features for classifier that is trained on a smaller corpus with manually annotated sentences. The method we propose advances the state-of-the art accuracy for Croatian sentence boundary detection on news corpora to 99.5%.

Keywords

  • Unsupervised Method
  • Lexical Feature
  • Sentence Boundary
  • Natural Language Processing Task
  • Brown Corpus

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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© 2012 Springer-Verlag Berlin Heidelberg

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Šarić, F., Šnajder, J., Dalbelo Bašić, B. (2012). Optimizing Sentence Boundary Detection for Croatian. In: Sojka, P., Horák, A., Kopeček, I., Pala, K. (eds) Text, Speech and Dialogue. TSD 2012. Lecture Notes in Computer Science(), vol 7499. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32790-2_12

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  • DOI: https://doi.org/10.1007/978-3-642-32790-2_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-32789-6

  • Online ISBN: 978-3-642-32790-2

  • eBook Packages: Computer ScienceComputer Science (R0)