Computer Science and Information Engineering, World Congress on (2009)
Los Angeles, California USA
Mar. 31, 2009 to Apr. 2, 2009
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/CSIE.2009.935
We present a new method for automatic term extraction which is based on training datasets created to build inductive models for term identiﬁcation. Existing approaches employ simple statistical and linguistic rules designed merely ad-hoc and are unable to utilize complex relations of linguistic units. In contrast to those approaches, our method does not require such manually ascribed rules of extraction. The data for our research is taken from the Czech National Corpus which is lemmatised and morphologically tagged. Statistical information (frequency, distribution etc.) is generated automatically and thus the only expert contribution needed is to label terms in the training dataset.The data mining software creates models that perform the extraction without any further human input. Additionally, feature ranking can serve as valuable aid for understanding of the extraction process and its future development and in terminology research.
automatic term extraction, data-mining, feature-ranking, corpus linguistics
Oleg Kovárík, Dominika Šrajerová, Václav Cvrcek, "Automatic Term Recognition Based on Data-Mining Techniques", Computer Science and Information Engineering, World Congress on, vol. 04, no. , pp. 453-457, 2009, doi:10.1109/CSIE.2009.935