Abstract
Representing the predicate argument structure of the medical domain (ASMD) is important for automatic text analyses. This work aims to describe the ASMD through verbs and transformational possibilities. Computer resources were constructed from 100 selected biomedical verbs (corpus CCM2009). Firstly, these were analyzed to determine the quantity and type of arguments, and classified these arguments into object classes (OC). Secondly, we established possible transformations for each ASMD. With this information, we created computer models on NooJ for the detection and automatic creation of ASMD in a corpus. This work involved the elaboration of electronic dictionaries, syntactic recognition, and generative grammars. The detection was performed on a corpus of 188,000 words conformed by texts from the gynecology and obstetrics area, achieving the following results: 100% accuracy, 96.92% coverage, and 98% F-measure. NooJ grammars provided grammatical sentences of each ASMD involving different transformations admitted by each particular class.
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Koza, W., Suy, C. (2021). Automatic Detection and Generation of Argument Structures Within the Medical Domain. In: Bigey, M., Richeton, A., Silberztein, M., Thomas, I. (eds) Formalizing Natural Languages: Applications to Natural Language Processing and Digital Humanities. NooJ 2021. Communications in Computer and Information Science, vol 1520. Springer, Cham. https://doi.org/10.1007/978-3-030-92861-2_17
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DOI: https://doi.org/10.1007/978-3-030-92861-2_17
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