Influence of Word Normalization on Text Classification
In this paper we focus our attention on the comparison of various lemmatization and stemming algorithms, which are oftenused in nature language processing (NLP). We describe thealgorithm in detail and compare it with other widely used algorithms for word normalization on two different corpora. Wepresent promising results obtained by our EWN-based lemmatization approach in comparison to other techniques. We alsodiscuss the influence of the word normalization on classification task in general.
Keywords: lemmatization, classification, EuroWordNet, stemming, word normalization
Year: 2006
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Full text [167 kB]Authors of this publication:
Michal Toman
E-mail: mtoman@kiv.zcu.cz
Michal graduated at UWB in 2003, specialized in software engineering. Currently, he is a PhD student interested in information retrieval, multilingual text processing, word sense disambiguation and knowledge discovery.
Roman Tesař
Phone: +420 377632479
E-mail: roman.tesar@gmail.com
WWW: http://www.sweb.cz/romant1/CV.pdf
Roman is a PhD student at the Department of Computer Science and Engineering, Faculty of Applied Sciences, University of West Bohemia in Pilsen, Czech Republic. His work is focused on the utilization of word n-grams in text classification and document filtering.
Karel Ježek
Phone: +420 377632475
E-mail: jezek_ka@kiv.zcu.cz
WWW: https://cs.wikipedia.org/wiki/Karel_Je%C5%BEek_(informatik)
Karel is the former group coordinator and a supervisor of PhD students working at research projects of this Group.
Related Projects:
Document Classification | |
| Authors: | Jiří Hynek, Karel Ježek, Michal Toman, Roman Tesař, Zdeněk Češka, Petr Grolmus |
| Desc.: | Use of inductive machine learning methods in classification of short text documents. |