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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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Wiesław Lubaszewski, Izabela Gatkowska, and Marcin Haręza

Human Association Network and Text Collection

A Network Extraction Driven by a Text-based Stimulus Word

 

 

Abstract: According to tradition, experimentally obtained human associations are analyzed in themselves, without relation to other linguistic data. In rare cases, human associations are used as the norm to evaluate the performance of algorithms, which generate associations on the basis of text corpora. This paper will describe a mechanical procedure to investigate how a word embedded in a text context may select associations in an experimentally built human association network. The results are rather encouraging: this procedure is able to distinguish those words in a text which ...

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