Adaptation of a Term Extractor to Arabic Specialised Texts: First Experiments and Limits

Neifar, Wafa; Hamon, Thierry; Zweigenbaum, Pierre; Khemakhem, Mariem Ellouze; Belguith, Lamia Hadrich

doi:10.1007/978-3-319-75477-2_16

Cited by 1 publication

(1 citation statement)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Hybrid approaches include, for instance, C_Value [ 34 ], C/NC_Value [ 13 ] methods, which combine statistical measures and linguistic based rules to extract multi-word and nested terms. In [ 6 , 30 ], the authors combine rule-based methods and dictionaries to extract terms from Spanish biomedical texts and specialised Arabic texts respectively.…”

Section: Related Workmentioning

confidence: 99%

ITEXT-BIO: Intelligent Term EXTraction for BIOmedical analysis

Kafando

Decoupes

Valentin

et al. 2021

Health Inf Sci Syst

View full text Add to dashboard Cite

Here, we introduce ITEXT-BIO, an intelligent process for biomedical domain terminology extraction from textual documents and subsequent analysis. The proposed methodology consists of two complementary approaches, including free and driven term extraction. The first is based on term extraction with statistical measures, while the second considers morphosyntactic variation rules to extract term variants from the corpus. The combination of two term extraction and analysis strategies is the keystone of ITEXT-BIO. These include combined intra-corpus strategies that enable term extraction and analysis either from a single corpus (intra), or from corpora (inter). We assessed the two approaches, the corpus or corpora to be analysed and the type of statistical measures used. Our experimental findings revealed that the proposed methodology could be used: (1) to efficiently extract representative, discriminant and new terms from a given corpus or corpora, and (2) to provide quantitative and qualitative analyses on these terms regarding the study domain.

show abstract

Section: Related Workmentioning

confidence: 99%