German Decompounding in a Difficult Corpus (original) (raw)

Abstract

Splitting compound words has proved to be useful in areas such as Machine Translation, Speech Recognition or Information Retrieval (IR). In the case of IR systems, they usually have to cope with noisy data, as user queries are usually written quickly and submitted without review. This work attempts at improving the current approaches for German decompounding when applied to query keywords. The results show an increase of more than 10% in accuracy compared to other state-of-the-art methods.

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Authors and Affiliations

  1. Google, Inc.,
    Enrique Alfonseca, Slaven Bilac & Stefan Pharies

Authors

  1. Enrique Alfonseca
  2. Slaven Bilac
  3. Stefan Pharies

Editor information

Alexander Gelbukh

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© 2008 Springer-Verlag Berlin Heidelberg

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Alfonseca, E., Bilac, S., Pharies, S. (2008). German Decompounding in a Difficult Corpus. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2008. Lecture Notes in Computer Science, vol 4919. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78135-6\_12

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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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