Cover not available
Article published In:
Terminology
Vol. 28:2 (2022) ► pp.299–327

Automatic medical term extraction from Vietnamese clinical texts

ORCID logo | Ho Chi Minh City University of Technology, Vietnam National University Ho Chi Minh City
| The University of Texas Health Science Center at Houston
| FPT University
| Tokyo University of Agriculture and Technology
| Unit Corporation
In this paper, we propose the first method for automatic Vietnamese medical term discovery and extraction from clinical texts. The method combines linguistic filtering based on our defined open patterns with nested term extraction and statistical ranking using C-value. It does not require annotated corpora, external data resources, parameter settings, or term length restriction. Beside its specialty in handling Vietnamese medical terms, another novelty is that it uses Pointwise Mutual Information to split nested terms and the disjunctive acceptance condition to extract them. Evaluated on real Vietnamese electronic medical records, it achieves a precision of about 74% and recall of about 92% and is proved stably effective with small datasets. It outperforms the previous works in the same category of not using annotated corpora and external data resources. Our method and empirical evaluation analysis can lay a foundation for further research and development in Vietnamese medical term discovery and extraction.
Keywords: automatic term extraction, electronic medical record, open linguistic pattern, pointwise mutual information, statistical ranking
Article outline
  • 1.Introduction
  • 2.Related works
    • 2.1Linguistics-based
    • 2.2Statistics-based
    • 2.3Machine learning-based
    • 2.4Hybrid
  • 3.The proposed method
    • 3.1Method overview
    • 3.2Preprocessing
    • 3.3Linguistics-based candidate term extraction
      • Part-of-Speech tagging
      • Open pattern-based term extraction
      • PMI-based nested term extraction
      • Stop word-based filtering
    • 3.4Statistics-based term ranking
  • 4.Empirical evaluation
    • 4.1Data descriptions
    • 4.2Experiment settings and results
      • Self-Evaluation
      • Comparative evaluation
  • 5.Conclusions
  • References
Published online: 9 June 2022
DOI logo
https://doi.org/10.1075/term.20037.vo
References
Arbabi, Aryan, David R. Adams, Sanja Fidler, and Michael Brudno
2019 “Identifying clinical terms in free-text notes using ontology-guided machine learning.” In RECOMB 2019, ed. by L. J. Cowen, LNBI, 114671: 19–34. Springer-Verlag. DOI logoGoogle Scholar
Aubin, Sophie, and Thierry Hamon
2006 “Improving term extraction with terminological resources.” In Proc the International Conference on Natural Language Processing: 380–387. DOI logoGoogle Scholar
Barrón-Cedeño, Alberto, Gerardo Sierra, Patrick Drouin, and Sophia Ananiadou
2009 “An improved automatic term recognition method for Spanish.” In CICLing 2009, ed. by A. Gelbukh, Lecture Notes in Computer Science 54491: 125–136. Springer-Verlag. DOI logoGoogle Scholar
Bonin, Francesca, Felice Dell’Orletta, Giulia Venturi, and Simonetta Montemagni
2010 “A contrastive approach to multi-word term extraction from domain corpora.” In Proc the 7th International Conference on Language Resources and Evaluation (LREC’10): 3222–3229.Google Scholar
Boulaknadel, Siham, Beatrice Daille, and Driss Aboutajdine
2008 “A multi-word term extraction program for Arabic language.” In Proc the 6th International Conference on Language Resources and Evaluation (LREC’08): 1485–1488.Google Scholar
Bouma, Gerlof
2009 “Normalized (pointwise) mutual information in collocation extraction.” In Proc GSCL: 31–40.Google Scholar
Bourigault, Didier
1992 “Surface grammatical analysis for the extraction of terminological noun phrases.” In Proc COLING-92: 977–981. DOI logoGoogle Scholar
Bourigault, Didier and Christian Jacquemin
1999 “TERM EXTRACTION + TERM CLUSTERING: an integrated platform for computer-aided terminology.” In Proc the 9th Conference on European Chapter of the Association for Computational Linguistics (EACL’99): 15–22. DOI logoGoogle Scholar
Cabré Castellví, M. Teresa
2003 “Theories of terminology: Their description, prescription and explanation.” Terminology 9 (2): 163–199. DOI logoGoogle Scholar
Chaimongkol, Panot and Akiko Aizawa
2013 “Utilizing LDA clustering for technical term extraction.” In Proc the 19th Annual Meeting of the Association for Natural Language Processing (ANLP): 686–689.Google Scholar
Chen, Jinying, and Hong Yu
2017 “Unsupervised ensemble ranking of terms in electronic health record notes based on their importance to patients.” Journal of Biomedical Informatics: 1–30. DOI logoGoogle Scholar
Chung, Teresa Mihwa
2003 “A corpus comparison approach for terminology extraction.” Terminology 9 (2): 221–246. DOI logoGoogle Scholar
Church, Kenneth Ward, and Patrick Hanks
1989 “Word association norms, mutual information, and lexicography.” In Proc the 27th Annual Meetings of the Association for Computational Linguistics: 76–83. DOI logoGoogle Scholar
Conrado, Merley S., Thiago A. S. Pardo, and Solange O. Rezende
2013 “Exploration of a rich feature set for automatic term extraction.” In MICAI 2013, ed. by F. Castro, A. Gelbukh, and M. González, LNAI, 82651: 342–354. Springer-Verlag. DOI logoGoogle Scholar
Dagan, Ido and Ken Church
1997 “Termight: coordinating humans and machines in bilingual terminology acquisition.” Machine Translation 121: 89–107. DOI logoGoogle Scholar
Daille, Béatrice
1994 “Study and implementation of combined techniques for automatic extraction of terminology.” In Proc the Balancing Act Workshop at the 32nd Annual Meeting of the ACL: 29–36.Google Scholar
Dias, Gaël
2003 “Multiword unit hybrid extraction.” In Proc the ACL 2003 Workshop on Multiword Expressions: Analysis, Acquisition and Treatment: 41–48. DOI logoGoogle Scholar
Dice, Lee R.
1945 “Measures of the amount of ecological association between species.” J. Ecology 261: 297–302. DOI logoGoogle Scholar
Diep, Quang Ban
2014 Vietnamese Grammar. Education Publisher, Vietnam. In Vietnamese.Google Scholar
Drouin, Patrick
2003 “Term extraction using non-technical corpora as a point of leverage.” Terminology 9 (1): 99–115. DOI logoGoogle Scholar
Fahmi, Ismail, Gosse Bouma, and Lonneke van der Plas
2007 “Using multilingual terms for biomedical term extraction.” In Proc the RANLP Workshop on Acquisition and Management of Multilingual Lexicons: 1–8.Google Scholar
Frantzi, Katerina T., and Sophia Ananiadou
1999 “The C-value/NC-value domain-independent method for multi-word term extraction.” Journal of Natural Language Processing 6 (3): 145–179. DOI logoGoogle Scholar
Frantzi, Katerina, Sophia Ananiadou, and Hideki Mima
2000 “Automatic recognition of multi-word terms: the C-value/NC-value method.” Int J Digit Libr 31: 115–130. DOI logoGoogle Scholar
Gao, Yuze, and Yu Yuan
2019 “Feature-less end-to-end nested term extraction.” In Proc the International Conference on Natural Language Processing and Chinese Computing: 607–616. DOI logoGoogle Scholar
He, Yulan
2016 “Extracting topical phrases from clinical documents.” In Proc the 30th AAAI Conf on Artificial Intelligence: 2957–2963. DOI logoGoogle Scholar
Heylen, Kris, and Dirk De Hertog
2015 “Automatic term extraction.” In Handbook of Terminology, ed. by H. J. Kockaert and F. Steurs, Vol. 11, 203–221. John Benjamins. DOI logoGoogle Scholar
Kageura, Kyo, and Bin Umino
1996 “Methods of automatic term recognition – a review.” Terminology. International Journal of Theoretical and Applied Issues in Specialized Communication 3(2): 259–289. DOI logoGoogle Scholar
Krauthammer, Michael, and Goran Nenadic
2004 “Term identification in the biomedical literature.” Journal of Biomedical Informatics 371: 512–526. DOI logoGoogle Scholar
Le, Hong Phuong
2016 “Vitk: a Vietnamese text processing toolkit.” (Jan. 2016) Retrieved Jan 01, 2016 from [URL]
Liu, Liangliang, Xiaojing Wu, Hui Liu, Xinyu Cao, Haitao Wang, Hongwei Zhou, and Qi Xie
2020 “A semi-supervised approach for extracting TCM clinical terms based on feature words.” BMC Medical Informatics and Decision Making 20 (Suppl 3): 118. DOI logoGoogle Scholar
Liu, Wei, Bo Chuen Chung, Rui Wang, Jonathon Ng, and Nigel Morlet
2015 “A genetic algorithm enabled ensemble for unsupervised medical term extraction from clinical letters.” Health Inf Sci Syst 3 (5): 1–14. DOI logoGoogle Scholar
Lossio-Ventura, Juan Antonio, Clement Jonquet, Mathieu Roche, and Maguelonne Teisseire
2016 “Biomedical term extraction: overview and a new methodology.” Information Retrieval Journal, Medical Information Retrieval 19 (1): 59–99. DOI logoGoogle Scholar
Maclean, Diana Lynn, and Jeffrey Heer
2013 “Identifying medical terms in patient-authored text: a crowdsourcing-based approach.” J Am Med Inform Assoc: 1–8. DOI logoGoogle Scholar
Marciniak, Malgorzata, and Agnieszka Mykowiecka
2014 “Terminology extraction from medical texts in Polish.” Journal of Biomedical Semantics 5 (24): 1–14. DOI logoGoogle Scholar
2015 “Nested term recognition driven by word connection strength.” Terminology 21 (2): 1–31.Google Scholar
Maynard, Diana, and Sophia Ananiadou
2001 “TRUCKS: a model for automatic multi-word term recognition.” Journal of Natural Language Processing 8 (1): 101–125. DOI logoGoogle Scholar
McInnes, Bridget T., Ted Pedersen, and Serguei V. Pakhomov
2007 “Determining the syntactic structure of medical terms in clinical notes.” In Proc the ACL 2007 Workshop on Biological, Translational, and Clinical Language Processing (BioNLP 2007): 9–16. DOI logoGoogle Scholar
Mihalcea, Rada, and Paul Tarau
2004 “TextRank: Bringing order into text.” In Proc the 2004 Conference on Empirical Methods in Natural Language Processing: 404–411.Google Scholar
Nguyen, Bao An, and Don-Lin Yang
2012 “A semi-automatic approach to construct Vietnamese ontology from online text.” The International Review of Research in Open and Distributed Learning 13 (5): 148–172. DOI logoGoogle Scholar
Nguyen, Hong Son, Minh Hieu Le, Chan Quan Loi Lam, and Trong Hai Duong
2017 “Smart interactive search for Vietnamese disease by using data mining-based ontology.” Journal of Information and Telecommunication 1 (2): 176–191. DOI logoGoogle Scholar
Nguyen, Minh Hiep, Huyen Nguyen Thi Minh, and Quyen Ngo The
2018 “Building Resources for Vietnamese Clinical Text Processing.” Computación y Sistemas 22 (4): 1287–1294.Google Scholar
Nguyen, Minh-Tien, and Tri-Thanh Nguyen
2015 “DESRM: a disease extraction system for real-time monitoring.” International Journal of Computational Vision and Robotics 5 (3): 282–301. DOI logoGoogle Scholar
Oliver, Antoni, and Mercè Vàzquez
2015 “TBXTools: a free, fast and flexible tool for automatic terminology extraction.” In Proc Recent Advances in Natural Language Processing: 473–479.Google Scholar
2020 “TermEval 2020: Using TSR Filtering Method to Improve Automatic Term Extraction.” In Proc the 6th International Workshop on Computational Terminology (COMPUTERM 2020): 106–113.Google Scholar
Pei, Jian, Jiawei Han, Behzad Mortazavi-Asl, Helen Pinto, Qiming Chen, Umeshwar Dayal, and Mei-Chun Hsu
2001 “PrefixSpan: Mining sequential patterns efficiently by Prefix-Projected Pattern Growth.” In Proc the 17th International Conference on Data Engineering: 1–10.Google Scholar
Periñán-Pascual, Carlos, and Eva M. Mestre-Mestre
2015 “DEXTER: Automatic extraction of domain-specific glossaries for language teaching.” Procedia – Social and Behavioral Sciences 1981: 377–385. DOI logoGoogle Scholar
Repar, Andraž, Vid Podpečan, Anze Vavpetič, Nada Lavrač, and Senja Pollak
2019 “TermEnsembler: an ensemble learning approach to bilingual term extraction and alignment.” Terminology 25 (1): 93–120.Google Scholar
Samy, Doaa, Antonio Moreno-Sandoval, Conchi Bueno-Díaz, Marta Garrote-Salazar, and José M. Guirao
2012 “Medical term extraction in an Arabic medical corpus.” In Proc the 8th International Conference on Language Resources and Evaluation (LREC’12): 640–645.Google Scholar
Terryn, Ayla Rigouts, Patrick Drouin, Véronique Hoste, and Els Lefever
2019 “Analysing the impact of supervised machine learning on automatic term extraction: HAMLET vs TermoStat.” In Proc Recent Advances in Natural Language Processing: 1012–1021. DOI logoGoogle Scholar
Terryn, Ayla Rigouts, Véronique Hoste, Joost Buysschaert, Robert Vander Stichele, Elise Van Campen, and Els Lefever
2019 “Validating multilingual hybrid automatic term extraction for search engine optimization: the use of EBM-GUIDELINES.” Argentinian Journal of Applied Linguistics: 93–108.Google Scholar
Terryn, Ayla Rigouts, Véronique Hoste, and Els Lefever
2018 “A gold standard for multilingual automatic term extraction from comparable corpora: term structure and translation equivalents.” In Proc the 11th International Conference on Language Resources and Evaluation (LREC 2018): 1803–1808.Google Scholar
Vàzquez, Mercè, and Antoni Oliver
2018 “Improving term candidates selection using terminological tokens.” Terminology 24 (1): 122–147.Google Scholar
Vivaldi, Jordi, Lluís Màrquez, and Horacio Rodríguez
2001 “Improving term extraction by system combination using boosting.” In ECML 2001, ed. by L. De Raedt and P. Flach, LNAI, Vol. 21671, 515–526. Springer-Verlag. DOI logoGoogle Scholar
Zhang, Xing, Yan Song, and Alex Chengyu Fang
2010 “Term recognition using conditional random fields.” In Proc the 6th International Conference on Natural Language Processing and Knowledge Engineering: 1–6. DOI logoGoogle Scholar
Zhang, Ziqi, Jie Gao, and Fabio Ciravegna
2017 “SemRe-Rank: improving automatic term extraction by incorporating semantic relatedness with personalised PageRank.” ACM Trans Knowl Discov Data 9 (4): 1–40. DOI logoGoogle Scholar