TY - GEN
T1 - Automatic Detection and Generation of Argument Structures Within the Medical Domain
AU - Koza, Walter
AU - Suy, Constanza
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - Biomedical domain
KW - Lexicon-grammar
KW - NooJ
KW - Predicate argument structure
UR - http://www.scopus.com/inward/record.url?scp=85123314745&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-92861-2_17
DO - 10.1007/978-3-030-92861-2_17
M3 - Conference contribution
AN - SCOPUS:85123314745
SN - 9783030928605
T3 - Communications in Computer and Information Science
SP - 198
EP - 207
BT - Formalizing Natural Languages
A2 - Bigey, Magali
A2 - Richeton, Annabel
A2 - Silberztein, Max
A2 - Thomas, Izabella
PB - Springer Science and Business Media Deutschland GmbH
T2 - 15th International Conference, NooJ 2021
Y2 - 9 June 2021 through 11 June 2021
ER -