Abstract
This paper presents a quantitative performance analysis of two different approaches to the lemmatization of the Czech text data. The first one is based on manually prepared dictionary of lemmas and set of derivation rules while the second one is based on automatic inference of the dictionary and the rules from training data. The comparison is done by evaluating the mean Generalized Average Precision (mGAP) measure of the lemmatized documents and search queries in the set of information retrieval (IR) experiments. Such method is suitable for efficient and rather reliable comparison of the lemmatization performance since a correct lemmatization has proven to be crucial for IR effectiveness in highly inflected languages. Moreover, the proposed indirect comparison of the lemmatizers circumvents the need for manually lemmatized test data which are hard to obtain and also face the problem of incompatible sets of lemmas across different systems.
This research was supported by the Min. of Education of the Czech Republic, project. No. MŠMT LC536, by the grant of the University of West Bohemia, project No. SGS-2010-054 and by the Grant Agency of Academy of Sciences of the Czech Republic., project. No. 1ET101470416.
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Kanis, J., Skorkovská, L. (2010). Comparison of Different Lemmatization Approaches through the Means of Information Retrieval Performance . In: Sojka, P., Horák, A., Kopeček, I., Pala, K. (eds) Text, Speech and Dialogue. TSD 2010. Lecture Notes in Computer Science(), vol 6231. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15760-8_13
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