In this paper, we present an experiment dealing with corpus-based construction of “differential ontologies”, which are organised according to semantic similarity and differential features. We argue that knowledge-rich defining contexts can be useful to help an ontology modeller in his task. We present a method, based on lexico-syntactic patterns, to spot such contexts in a corpus, then identify the terms they relate (definiendum and genus or “characteristics”) and the semantic relation that links them. We also show how potential co-hyponyms can be detected on the basis of shared words in their definiens. We evaluate the extracted defining sentences, semantic relations and co-hyponyms on a test corpus focusing on childhood and on an evaluation corpus about dietetics (both corpora are French). Definition extraction obtains 50% precision and recall of approximately 40%. Semantic relation identification reaches an average of 48% precision, and co-hyponyms 23.5%. We discuss the results of these experiments and conclude on perspectives for future work.
Compson, Zacchaeus G., Wendy A. Monk, Colin J. Curry, Dominique Gravel, Alex Bush, Christopher J.O. Baker, Mohammad Sadnan Al Manir, Alexandre Riazanov, Mehrdad Hajibabaei, Shadi Shokralla, Joel F. Gibson, Sonja Stefani, Michael T.G. Wright & Donald J. Baird
2018. Linking DNA Metabarcoding and Text Mining to Create Network-Based Biomonitoring Tools: A Case Study on Boreal Wetland Macroinvertebrate Communities. In Next Generation Biomonitoring: Part 2 [Advances in Ecological Research, 59], ► pp. 33 ff.
2014. Hunting for a linguistic phantom. Terminology. International Journal of Theoretical and Applied Issues in Specialized Communication 20:2 ► pp. 198 ff.
2009. 2009 Latin American Web Congress, ► pp. 217 ff.
Baneyx, Audrey, Jean Charlet & Marie-Christine Jaulent
2007. Building an ontology of pulmonary diseases with natural language processing tools using textual corpora. International Journal of Medical Informatics 76:2-3 ► pp. 208 ff.
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