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Natural Language Processing and Cognitive Science by Michael Zock, Mai Yanagimura, Elena Yagunova, Svetlana Volskaya, Jordi Turmo, Susanna Tron, Rocco Tripodi, Radu Topor, Ludovic Tanguy, David Suendermann-Oeft, Elizabeth Rosenfeld, Horacio Rodriguez, Livio Robaldo, Solange O. Rezende, Michaela Regneri, Lidia Pivovarova, Marcello Pelillo, Catherine Pelachaud, Thiago S. Pardo, Ali M. Naderi, Sachiyo Muranishi, Michael Muck, Martin Mory, Francois Morlane-Hondere, Tarek Mehrez, Wieslaw Lubaszewski, Patrick Lange, Shingo Kuroiwa, Manfred Klenner, Diane King, Mohamed Hamed Kholief, Alexei V. Ivanov, Yasuo Horiuchi, Nora Hollenstein, Myriam Hernandez Alvarez, Jiri Havelka, Nabil Hathout, Marcin Hareza, Jose M. Gomez, Emiliano Giovannetti, Nadine Glas, Daniela Gifu, Izabela Gatkowska, Jean-Gabriel Ganascia, Daisuke Furukawa, Richard Frost, Cécile Fabre, Ahmed Magdy Ezzeldin, Yasser El-Sonbaty, Luigi Di Caro, Matthew Crocker, Dan Cristea, Jan Curin, Conrado S. Merley, Pawel Chrzazcz, Jesus Calvillo, Mohamed Amine Boukhaled, Guido Boella, Jared Bernstein, Alessia Bellusci, Andrea Bellandi, Raimo Bakis, Eniafe Festus Ayetiran, Michael Amsler, Nabil Abdullah, César Aguilar, Olga Acosta, Rodolfo Delmonte, Bernadette Sharp

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Merley S. Conrado, Thiago A. S. Pardo, and Solange O. Rezende

The Main Challenge of Semi-Automatic Term Extraction Methods

Abstract: Term extraction is the basis for many tasks such as building of taxonomies, ontologies and dictionaries, for translation, organization and retrieval of textual data. This paper studies the main challenge of semi-automatic term extraction methods, which is the difficulty to analyze the rank of candidates created by these methods. With the experimental evaluation performed in this work, it is possible to fairly compare a wide set of semi-automatic term extraction methods, which allows other future investigations. Additionally, we discovered which level of knowledge and threshold should be adopted for these methods ...

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