Exploring terminological relations between multi-word terms in distributional semantic models

References

1. Allen, Carl, and Timothy Hospedales
   2019 “Analogies Explained: Towards Understanding Word Embeddings.” InInternational Conference on Machine Learning, 223–31. Long Beach, California, USA: PMLR.
   [Google Scholar]
2. Arefyev, Nikolay, Boris Sheludko, Alexander Podolskiy, and Alexander Panchenko
   2020 “A Comparative Study of Lexical Substitution Approaches Based on Neural Language Models.” ArXiv Preprint ArXiv:2006.00031.
   [Google Scholar]
3. Barrière, Caroline
   2004 “Knowledge-Rich Contexts Discovery.” InConference of the Canadian Society for Computational Studies of Intelligence, Canadian AI, 187–201. London, Ontario, Canada: Springer. 10.1007/978‑3‑540‑24840‑8_14
   https://doi.org/10.1007/978-3-540-24840-8_14 [Google Scholar]
4. Bernier-Colborne, Gabriel
   2017 “Aide à l’identification de Relations Lexicales Au Moyen de La Sémantique Distributionnelle et Son Application à Un Corpus Bilingue Du Domaine de l’environnement.” PhD Diss., Université de Montréal.
   [Google Scholar]
5. Bernier-Colborne, Gabriel, and Patrick Drouin
   2016 “Évaluation des modèles sémantiques distributionnels : le cas de la dérivation syntaxique (Evaluation of distributional semantic models : The case of syntactic derivation).” InActes de la conférence conjointe JEP-TALN-RECITAL 2016. volume 2 : TALN (Articles longs), 125–38. Paris, France: AFCP – ATALA.
   [Google Scholar]
6. Bojanowski, Piotr, Edouard Grave, Armand Joulin, and Tomas Mikolov
   2017 “Enriching Word Vectors with Subword Information.” Transactions of the Association for Computational Linguistics51: 135–46. 10.1162/tacl_a_00051
   https://doi.org/10.1162/tacl_a_00051 [Google Scholar]
7. Bouraoui, Zied, Jose Camacho-Collados, and Steven Schockaert
   2020 “Inducing Relational Knowledge from BERT.” InProceedings of the AAAI Conference on Artificial Intelligence, 341:7456–63. New York, New York, United States. 10.1609/aaai.v34i05.6242
   https://doi.org/10.1609/aaai.v34i05.6242 [Google Scholar]
8. Bouraoui, Zied, Shoaib Jameel, and Steven Schockaert
   2018 “Relation Induction in Word Embeddings Revisited.” InProceedings of the 27th International Conference on Computational Linguistics, 1627–37. Santa Fe, New Mexico, USA: Association for Computational Linguistics.
   [Google Scholar]
9. Bourigault, Didier
   2002 “UPERY : Un Outil D’analyse Distributionnelle Étendue Pour La Construction D’ontologies à Partir de Corpus.” InActes de La 9e Conférence Sur Le Traitement Automatique Des Langues Naturelles. Articles Longs, 75–84. Nancy, France: ATALA.
   [Google Scholar]
10. Bullinaria, John A., and Joseph P. Levy
    2012 “Extracting Semantic Representations from Word Co-Occurrence Statistics: Stop-Lists, Stemming, and SVD.” Behavior Research Methods44 (3): 890–907. 10.3758/s13428‑011‑0183‑8
    https://doi.org/10.3758/s13428-011-0183-8 [Google Scholar]
11. Chaudhri, Vinay K., Justin Xu, Han Lin Aung, and Sajana Weerawardhena
    2022 “A Corpus of Biology Analogy Questions as a Challenge for Explainable AI.” InBridging Human Intelligence and Artificial Intelligence, edited byMark V. Albert, Lin Lin, Michael J. Spector, and Lemoyne S. Dunn, 327–37. Educational Communications and Technology: Issues and Innovations. Cham: Springer International Publishing. 10.1007/978‑3‑030‑84729‑6_23
    https://doi.org/10.1007/978-3-030-84729-6_23 [Google Scholar]
12. Chen, Zhiwei, Zhe He, Xiuwen Liu, and Jiang Bian
    2018 “Evaluating Semantic Relations in Neural Word Embeddings with Biomedical and General Domain Knowledge Bases.” BMC Medical Informatics and Decision Making18 (2): 53–68.
    [Google Scholar]
13. Cram, Damien, and Béatrice Daille
    2016 “Terminology Extraction with Term Variant Detection.” InProceedings of ACL-2016 System Demonstrations, 13–18. Berlin, Germany: Association for Computational Linguistics. 10.18653/v1/P16‑4003
    https://doi.org/10.18653/v1/P16-4003 [Google Scholar]
14. Daille, Béatrice
    2017 Term Variation in Specialised Corpora: Characterisation, Automatic Discovery and Applications. Vol.191. Amsterdam / Philadelphia: John Benjamins Publishing Company. 10.1075/tlrp.19
    https://doi.org/10.1075/tlrp.19 [Google Scholar]
15. Daille, Béatrice, and Amir Hazem
    2014 “Semi-Compositional Method for Synonym Extraction of Multi-Word Terms.” InProceedings of the Ninth International Conference on Language Resources and Evaluation (LREC’14), 1202–7. Reykjavik, Iceland: European Language Resources Association (ELRA). www.lrec-conf.org/proceedings/lrec2014/pdf/679_Paper.pdf
    [Google Scholar]
16. Depraetere, Ilse
    2019 “Meaning in Context and Contextual Meaning: A Perspective on the Semantics-Pragmatics Interface Applied to Modal Verbs.” Anglophonia. French Journal of English Linguistics, no.28. 10.4000/anglophonia.2453
    https://doi.org/10.4000/anglophonia.2453 [Google Scholar]
17. Devlin, Jacob, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova
    2019 “BERT: Pre-Training of Deep Bidirectional Transformers for Language Understanding.” InProceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), 4171–86. Minneapolis, Minnesota: Association for Computational Linguistics. 10.18653/v1/N19‑1423
    https://doi.org/10.18653/v1/N19-1423 [Google Scholar]
18. Espinosa Anke, Luis, Joan Codina-Filba, and Leo Wanner
    2021 “Evaluating Language Models for the Retrieval and Categorization of Lexical Collocations.” InProceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, 1406–17. Online: Association for Computational Linguistics. 10.18653/v1/2021.eacl‑main.120
    https://doi.org/10.18653/v1/2021.eacl-main.120 [Google Scholar]
19. Ferret, Olivier
    2021 “Exploration des relations sémantiques sous-jacentes aux plongements contextuels de mots (Exploring semantic relations underlying contextual word embeddings).” InActes de la 28e Conférence sur le Traitement Automatique des Langues Naturelles. Volume 1 : conférence principale, 26–36. Lille, France: ATALA.
    [Google Scholar]
20. Fu, Ruiji, Jiang Guo, Bing Qin, Wanxiang Che, Haifeng Wang, and Ting Liu
    2014 “Learning Semantic Hierarchies via Word Embeddings.” InProceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 1199–1209. Baltimore, Maryland: Association for Computational Linguistics. 10.3115/v1/P14‑1113
    https://doi.org/10.3115/v1/P14-1113 [Google Scholar]
21. Gábor, Kata, Davide Buscaldi, Anne-Kathrin Schumann, Behrang QasemiZadeh, Haifa Zargayouna, and Thierry Charnois
    2018 “SemEval-2018 Task 7: Semantic Relation Extraction and Classification in Scientific Papers.” InProceedings of The 12th International Workshop on Semantic Evaluation, 679–88. New Orleans, Louisiana. 10.18653/v1/S18‑1111
    https://doi.org/10.18653/v1/S18-1111 [Google Scholar]
22. Gladkova, Anna, Aleksandr Drozd, and Satoshi Matsuoka
    2016 “Analogy-Based Detection of Morphological and Semantic Relations with Word Embeddings: What Works and What Doesn’t.” InProceedings of the NAACL Student Research Workshop, 8–15. San Diego, California. 10.18653/v1/N16‑2002
    https://doi.org/10.18653/v1/N16-2002 [Google Scholar]
23. Grabar, Natalia, and Thierry Hamon
    2006 “Terminology Structuring through the Derivational Morphology.” InInternational Conference on Natural Language Processing (in Finland), 652–63. Berlin Heidelberg: Springer. 10.1007/11816508_65
    https://doi.org/10.1007/11816508_65 [Google Scholar]
24. Harris, Zellig S.
    1954 “Distributional Structure.” Word10 (2–3): 146–62. 10.1080/00437956.1954.11659520
    https://doi.org/10.1080/00437956.1954.11659520 [Google Scholar]
25. Hashimoto, Kazuma, Pontus Stenetorp, Makoto Miwa, and Yoshimasa Tsuruoka
    2015 “Task-Oriented Learning of Word Embeddings for Semantic Relation Classification.” InProceedings of the Nineteenth Conference on Computational Natural Language Learning, 268–78. Beijing, China: Association for Computational Linguistics. 10.18653/v1/K15‑1027
    https://doi.org/10.18653/v1/K15-1027 [Google Scholar]
26. Hazem, Amir, and Béatrice Daille
    2018 “Word Embedding Approach for Synonym Extraction of Multi-Word Terms.” InProceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018), 297–303. Miyazaki, Japan: European Language Resources Association (ELRA).
    [Google Scholar]
27. Hmida, Firas, Emmanuel Morin, and Béatrice Daille
    2015 “Extraction de Contextes Riches En Connaissances En Corpus Spécialisés.” InActes de La 22e Conférence Sur Le Traitement Automatique Des Langues Naturelles. Articles Courts, 109–15. Caen, France: ATALA.
    [Google Scholar]
28. Hou, Jiaqi, Xin Li, Haipeng Yao, Haichun Sun, Tianle Mai, and Rongchen Zhu
    2020 “Bert-Based Chinese Relation Extraction for Public Security.” IEEE Access81: 132367–75. 10.1109/ACCESS.2020.3002863
    https://doi.org/10.1109/ACCESS.2020.3002863 [Google Scholar]
29. Jameel, Shoaib, Zied Bouraoui, and Steven Schockaert
    2017 “Modeling Semantic Relatedness Using Global Relation Vectors.” ArXiv Preprint ArXiv:1711.05294.
    [Google Scholar]
30. Kilgarriff, Adam, Miloš Husák, Katy McAdam, Michael Rundell, and Pavel Rychlý
    2008 “GDEX: Automatically Finding Good Dictionary Examples in a Corpus.” InProceedings of the XIII EURALEX International Congress, 425–32. Barcelona, Spain: Documenta Universitaria.
    [Google Scholar]
31. Köper, Maximilian, Christian Scheible, and Sabine Schulte im Walde
    2015 “Multilingual Reliability and ‘Semantic’ Structure of Continuous Word Spaces.” InProceedings of the 11th International Conference on Computational Semantics, 40–45. London, UK: Association for Computational Linguistics.
    [Google Scholar]
32. Kudo, Taku, and John Richardson
    2018 “SentencePiece: A Simple and Language Independent Subword Tokenizer and Detokenizer for Neural Text Processing.” InProceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, 66–71. Brussels, Belgium: Association for Computational Linguistics. 10.18653/v1/D18‑2012
    https://doi.org/10.18653/v1/D18-2012 [Google Scholar]
33. Lafourcade, Mathieu, and Lionel Ramadier
    2016 “Semantic Relation Extraction with Semantic Patterns Experiment on Radiology Reports.” InProceedings of the Tenth International Conference on Language Resources and Evaluation (LREC’16), 4578–82. Portorož, Slovenia: European Language Resources Association (ELRA).
    [Google Scholar]
34. Lenci, Alessandro
    2008 “Distributional Semantics in Linguistic and Cognitive Research.” Italian Journal of Linguistics20 (1): 1–31.
    [Google Scholar]
35. Lenci, Alessandro, and Giulia Benotto
    2012 “Identifying Hypernyms in Distributional Semantic Spaces.” In*SEM 2012: The First Joint Conference on Lexical and Computational Semantics – Volume 1: Proceedings of the Main Conference and the Shared Task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation (SemEval 2012), 75–79. Montréal, Canada: Association for Computational Linguistics.
    [Google Scholar]
36. Levy, Omer, Yoav Goldberg
    2014 “Linguistic Regularities in Sparse and Explicit Word Representations.” InProceedings of the Eighteenth Conference on Computational Natural Language Learning, 171–80. Ann Arbor, Michigan: Association for Computational Linguistics. 10.3115/v1/W14‑1618.
    https://doi.org/10.3115/v1/W14-1618 [Google Scholar]
37. Levy, Omer, Yoav Goldberg, and Ido Dagan
    2015 “Improving Distributional Similarity with Lessons Learned from Word Embeddings.” Transactions of the Association for Computational Linguistics31: 211–25. 10.1162/tacl_a_00134
    https://doi.org/10.1162/tacl_a_00134 [Google Scholar]
38. Levy, Omer, Steffen Remus, Chris Biemann, and Ido Dagan
    2015 “Do Supervised Distributional Methods Really Learn Lexical Inference Relations?” InProceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 970–76. Denver, Colorado: Association for Computational Linguistics. 10.3115/v1/N15‑1098
    https://doi.org/10.3115/v1/N15-1098 [Google Scholar]
39. L’Homme, Marie-Claude
    2004 La Terminologie: Principes et Techniques. Montréal: Pum. 10.4000/books.pum.10693
    https://doi.org/10.4000/books.pum.10693 [Google Scholar]
40. L’Homme, Marie-Claude
    2020 Lexical Semantics for Terminology: An Introduction. Vol.201. Amsterdam / Philadelphia: John Benjamins Publishing Company. 10.1075/tlrp.20
    https://doi.org/10.1075/tlrp.20 [Google Scholar]
41. Martin, Louis, Benjamin Muller, Pedro Javier Ortiz Suárez, Yoann Dupont, Laurent Romary, Éric de la Clergerie, Djamé Seddah, and Benoît Sagot
    2020 “CamemBERT: A Tasty French Language Model.” InProceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 7203–19. Online: Association for Computational Linguistics. 10.18653/v1/2020.acl‑main.645
    https://doi.org/10.18653/v1/2020.acl-main.645 [Google Scholar]
42. Meyer, Ingrid
    2001 “Extracting Knowledge-Rich Contexts for Terminography.” InRecent Advances in Computational Terminology, edited byDidier Bourigault, Christian Jacquemin and Marie-Claude L’Homme, 279–302. Amsterdam / Philadelphia: John Benjamins. 10.1075/nlp.2.15mey
    https://doi.org/10.1075/nlp.2.15mey [Google Scholar]
43. Mickus, Timothee, Denis Paperno, Mathieu Constant, and Kees van Deemter
    2020 “What Do You Mean, BERT?” InProceedings of the Society for Computation in Linguistics 2020, 279–90. New York, New York: Association for Computational Linguistics.
    [Google Scholar]
44. Mikolov, Tomas, Wen-tau Yih, and Geoffrey Zweig
    2013 “Linguistic Regularities in Continuous Space Word Representations.” InProceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 746–51. Atlanta, Georgia: Association for Computational Linguistics.
    [Google Scholar]
45. Morin, Emmanuel, and Christian Jacquemin
    1999 “Projecting Corpus-Based Semantic Links on a Thesaurus.” InProceedings of the 37th Annual Meeting of the Association for Computational Linguistics on Computational Linguistics, 389–96. USA: Association for Computational Linguistics. 10.3115/1034678.1034739
    https://doi.org/10.3115/1034678.1034739 [Google Scholar]
46. Morlane-Hondère, François, and Cécile Fabre
    2012 “Le Test de Substituabilité à l’épreuve Des Corpus: Utiliser l’analyse Distributionnelle Automatique Pour l’étude Des Relations Lexicales.” In3e CMLF, 11:1001–15. France: EDP Sciences. 10.1051/shsconf/20120100293
    https://doi.org/10.1051/shsconf/20120100293 [Google Scholar]
47. Paullada, Amandalynne, Bethany Percha, and Trevor Cohen
    2020 “Improving Biomedical Analogical Retrieval with Embedding of Structural Dependencies.” InProceedings of the 19th SIGBioMed Workshop on Biomedical Language Processing, 38–48. Online: Association for Computational Linguistics. 10.18653/v1/2020.bionlp‑1.4
    https://doi.org/10.18653/v1/2020.bionlp-1.4 [Google Scholar]
48. Peters, Matthew E., Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer
    2018 “Deep Contextualized Word Representations.” InProceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), 2227–37. New Orleans, Louisiana: Association for Computational Linguistics. 10.18653/v1/N18‑1202
    https://doi.org/10.18653/v1/N18-1202 [Google Scholar]
49. Peters, Matthew E., Mark Neumann, Luke Zettlemoyer, and Wen-tau Yih
    2018 “Dissecting Contextual Word Embeddings: Architecture and Representation.” InProceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, 1499–1509. Brussels, Belgium: Association for Computational Linguistics. 10.18653/v1/D18‑1179
    https://doi.org/10.18653/v1/D18-1179 [Google Scholar]
50. Polguère, Alain
    2016 Lexicologie et Sémantique Lexicale: Notions Fondamentales. Montréal: Presses de l’Université de Montréal. 10.2307/j.ctv69t90p
    https://doi.org/10.2307/j.ctv69t90p [Google Scholar]
51. Qiang, Jipeng, Yun Li, Yi Zhu, Yunhao Yuan, and Xindong Wu
    2019 “A Simple BERT-Based Approach for Lexical Simplification.” ArXivabs/1907.06226.
    [Google Scholar]
52. Qiao, Bo, Zhuoyang Zou, Yu Huang, Kui Fang, Xinghui Zhu, and Yiming Chen
    2022 “A Joint Model for Entity and Relation Extraction Based on BERT.” Neural Comput. Appl. 34 (5): 3471–81. 10.1007/s00521‑021‑05815‑z
    https://doi.org/10.1007/s00521-021-05815-z [Google Scholar]
53. Roller, Stephen, Katrin Erk, and Gemma Boleda
    2014 “Inclusive yet Selective: Supervised Distributional Hypernymy Detection.” InProceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers, 1025–36. Dublin, Ireland: Dublin City University and Association for Computational Linguistics.
    [Google Scholar]
54. Santos, Cicero Nogueira dos, Bing Xiang, and Bowen Zhou
    2015 “Classifying Relations by Ranking with Convolutional Neural Networks.” InProceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), 626–34. Beijing, China: Association for Computational Linguistics. 10.3115/v1/P15‑1061
    https://doi.org/10.3115/v1/P15-1061 [Google Scholar]
55. Schick, Timo, and Hinrich Schütze
    2019 “Attentive Mimicking: Better Word Embeddings by Attending to Informative Contexts.” InProceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), 489–94. Minneapolis, Minnesota: Association for Computational Linguistics. 10.18653/v1/N19‑1048
    https://doi.org/10.18653/v1/N19-1048 [Google Scholar]
56. Schick, Timo, and Hinrich Schütze
    2020 “Rare Words: A Major Problem for Contextualized Embeddings and How to Fix It by Attentive Mimicking.” InProceedings of the AAAI Conference on Artificial Intelligence, 341:8766–74. New York, USA: AAAI Press. 10.1609/aaai.v34i05.6403
    https://doi.org/10.1609/aaai.v34i05.6403 [Google Scholar]
57. Shi, Peng, and Jimmy Lin
    2019 “Simple BERT Models for Relation Extraction and Semantic Role Labeling.” ArXiv Preprint ArXiv:1904.05255.
    [Google Scholar]
58. Sutskever, Ilya, Oriol Vinyals, and Quoc V. Le
    2014 “Sequence to Sequence Learning with Neural Networks.” InProceedings of the 27th International Conference on Neural Information Processing Systems – Volume 2, 3104–12. Montreal, Canada: MIT Press.
    [Google Scholar]
59. Turney, Peter D.
    2005 “Measuring Semantic Similarity by Latent Relational Analysis.” InProceedings of the 19th International Joint Conference on Artificial Intelligence, 1136–41. IJCAI’05. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc.
    [Google Scholar]
60. Verspoor, Cornelia M., Cliff Joslyn, and George J. Papcun
    2003 “The Gene Ontology as a Source of Lexical Semantic Knowledge for a Biological Natural Language Processing Application.” InSIGIR Workshop on Text Analysis and Search for Bioinformatics, 51–56. Toronto, Canada.
    [Google Scholar]
61. Vylomova, Ekaterina, Laura Rimell, Trevor Cohn, and Timothy Baldwin
    2016 “Take and Took, Gaggle and Goose, Book and Read: Evaluating the Utility of Vector Differences for Lexical Relation Learning.” InProceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 1671–82. Berlin, Germany: Association for Computational Linguistics. 10.18653/v1/P16‑1158
    https://doi.org/10.18653/v1/P16-1158 [Google Scholar]
62. Weeds, Julie, Daoud Clarke, Jeremy Reffin, David Weir, and Bill Keller
    2014 “Learning to Distinguish Hypernyms and Co-Hyponyms.” InProceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers, 2249–59. Dublin, Ireland: Dublin City University and Association for Computational Linguistics.
    [Google Scholar]
63. Weeds, Julie, et David Weir
    2003 « A General Framework for Distributional Similarity ». InProceedings of the 2003 Conference on Empirical Methods in Natural Language Processing, 81–88. EMNLP ’03. USA: Association for Computational Linguistics. 10.3115/1119355.1119366
    https://doi.org/10.3115/1119355.1119366 [Google Scholar]
64. Xue, Kui, Yangming Zhou, Zhiyuan Ma, Tong Ruan, Huanhuan Zhang, and Ping He
    2019 “Fine-Tuning BERT for Joint Entity and Relation Extraction in Chinese Medical Text.” In2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 892–97. San Diego, CA, USA: IEEE. 10.1109/BIBM47256.2019.8983370
    https://doi.org/10.1109/BIBM47256.2019.8983370 [Google Scholar]
65. Yao, Liang, Chengsheng Mao, and Yuan Luo
    2019 “KG-BERT: BERT for Knowledge Graph Completion.” ArXiv Preprint ArXiv:1909.03193.
    [Google Scholar]
66. Zhang, Li, Jun Li, and Chao Wang
    2017 “Automatic Synonym Extraction Using Word2Vec and Spectral Clustering.” In2017 36th Chinese Control Conference (CCC), 5629–32. Dalian, China: IEEE. 10.23919/ChiCC.2017.8028251
    https://doi.org/10.23919/ChiCC.2017.8028251 [Google Scholar]
67. Zhou, Wangchunshu, Tao Ge, Ke Xu, Furu Wei, and Ming Zhou
    2019 “BERT-Based Lexical Substitution.” InProceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 3368–73. Florence, Italy: Association for Computational Linguistics. 10.18653/v1/P19‑1328
    https://doi.org/10.18653/v1/P19-1328 [Google Scholar]
68. Zhu, Yongjun, Erjia Yan, and Fei Wang
    2017 “Semantic Relatedness and Similarity of Biomedical Terms: Examining the Effects of Recency, Size, and Section of Biomedical Publications on the Performance of Word2vec.” BMC Medical Informatics and Decision Making17 (1): 1–8. 10.1186/s12911‑017‑0498‑1
    https://doi.org/10.1186/s12911-017-0498-1 [Google Scholar]
69. Zweigenbaum, Pierre, and Natalia Grabar
    2000 “Liens Morphologiques et Structuration de Terminologie.” InIC 2000 : Ingénierie Des Connaissances, 325–34. Toulouse: Irit.
    [Google Scholar]