Abstract
This work presents a review, using bibliometric methods, of the state of research on natural language processing in Mexico, understanding this as the methods to process human language, including semantic techniques, statistical techniques or a combination of both. It includes an identification of the most productive groups in the country, the main actors involved in research, their collaboration links, as well as the main topics that are investigated. This last aspect is compared with the subjects reported in the international literature to identify differences and similarities on research interests between researchers working in Mexico and those reported in mainstream literature. We conclude with a characterization of research carried out in Mexico about language technologies and we found that the research subjects investigated are somewhat similar to those that are reported in the international literature when time is not considered in the analysis. Nevertheless, when this variable is included, there are practically no similarities in the subjects researched in México and those researched in the world.
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Data were collected in March 2018.
There are other types of documents, but they have very low frequencies and are not included.
References
Alejo-Machado OJ, Fernandez-Luna JM, Huete JF (2015) Bibliometric study of the scientific research on “Learning to Rank” between 2000 and 2013. Scientometrics 102(2):1669–1686. https://doi.org/10.1007/s11192-014-1467-4
Alom MZ, Taha TM, Yakopcic C, Westberg S, Sidike P, Nasrin MS, Hasan M, Van Essen BC, Awwal AAS, Asari VK (2019) A state-of-the-art survey on deep learning theory and architectures. Electronics 8 (3). https://doi.org/10.3390/electronics8030292
Barakhnin VB, Duisenbayeva AN, Kozhemyakina OY, Yergaliyev YN, Muhamedyev RI (2018) The automatic processing of the texts in natural language. Some bibliometric indicators of the current state of this research area. J Phys Conf Ser 1117:012001. https://doi.org/10.1088/1742-6596/1117/1/012001
Bhattacharya S (2019) Some salient aspects of machine learning research: a bibliometric analysis. J Scientometric Res 8(2):S85–S92. https://doi.org/10.5530/jscires.8.2.26
Börner K, Chen C, Boyack KW (2003) Visualizing knowledge domains. Ann Rev Inf Sci Technol 37(1):179–255. https://doi.org/10.1002/aris.1440370106
Callon M, Courtial J-P, Turner WA, Bauin S (1983) From translations to problematic networks: An introduction to co-word analysis. Information (Intl Soc Sci Council) 22(2):191–235. https://doi.org/10.1177/053901883022002003
Callon M, Courtial JP, Laville F (1991) Co–word analysis as a tool for describing the network of interactions between basic and technological research: the case of polymer chemistry. Scientometrics 22(1):155–205
Chen G, Xiao L (2016) Selecting publication keywords for domain analysis in bibliometrics: A comparison of three methods. J Informetr 10(1):212–223. https://doi.org/10.1016/j.joi.2016.01.006
Chen X, Ding R, Xu K, Wang S, Hao T, Zhou Y (2018) A bibliometric review of natural language processing empowered mobile computing. Wirel Commun Mob Comput 2018:1827074. https://doi.org/10.1155/2018/1827074
Cobo MJ, López-Herrera AG, Herrera-Viedma E, Herrera F (2011) An approach for detecting, quantifying, and visualizing the evolution of a research field: A practical application to the Fuzzy Sets Theory field. J Informetr 5(1):146–166. https://doi.org/10.1016/j.joi.2010.10.002
Crane D (1972) Invisible colleges; diffusion of knowledge in scientific communities. University of Chicago Press, Chicago
de Nooy W, Mrvar A, Batagelj V (2018) Exploratory social network analysis with pajek: revised and expanded edition for updated software, 3rd edn. Cambridge University Press, New York
de Solla Price DJ (1965) Networks of scientific papers. Science 149(3683):510–515. https://doi.org/10.1126/science.149.3683.510
dos Santos BS, Steiner MTA, Fenerich AT, Lima RHP (2019) Data mining and machine learning techniques applied to public health problems: A bibliometric analysis from 2009 to 2018. Comput Ind Eng 138. https://doi.org/10.1016/j.cie.2019.106120
Ellegaard O, Wallin JA (2015) The bibliometric analysis of scholarly production: How great is the impact? Scientometrics 105(3):1809–1831. https://doi.org/10.1007/s11192-015-1645-z
Freeman LC (1977) A set of measures of centrality based on betweenness. Sociometry 40(1):35–41. https://doi.org/10.2307/3033543
Freeman LC (1978) Centrality in social networks conceptual clarification. Soc Netw 1(3):215–239. https://doi.org/10.1016/0378-8733(78)90021-7
Gupta S, Gupta SK (2019) Abstractive summarization: An overview of the state of the art. Expert Syst Appl 121:49–65. https://doi.org/10.1016/j.eswa.2018.12.011
Henkel M, Perjons E, Sneiders E (2017) Examining the potential of language technologies in public organizations by means of a business and IT architecture model. Int J Inf Manag 37(1, Part A):1507–1516. https://doi.org/10.1016/j.ijinfomgt.2016.05.008
Io HN, Lee CB (2017) Chatbots and conversational agents: a bibliometric analysis. In: 2017 IEEE International Conference on Industrial Engineering and Engineering Management. International Conference on Industrial Engineering and Engineering Management IEEM. IEEE, New York, pp 215–219
Jia Y, Wang W, Liang J, Liu L, Chen Z, Zhang J, Chen T, Lei J (2018) Trends and characteristics of global medical informatics conferences from 2007 to 2017: A bibliometric comparison of conference publications from Chinese, American, European and the Global Conferences. Comput Methods Prog Biomed. 166:19–32. https://doi.org/10.1016/j.cmpb.2018.08.017
Kalantari A, Kamsin A, Kamaruddin HS, Ale Ebrahim N, Gani A, Ebrahimi A, Shamshirband S (2017) A bibliometric approach to tracking big data research trends. J Big Data 4(1):30. https://doi.org/10.1186/s40537-017-0088-1
Keramatfar A, Amirkhani H (2018) Bibliometrics of sentiment analysis literature. J Inf Sci 45(1):3–15. https://doi.org/10.1177/0165551518761013
Kleinberg J Bursty and hierarchical structure in streams. In: Proc. 8th ACM SIGKDD Intl. Conf. on Knowledge Discovery and Data Mining, 2002
Li K, Rollins J, Yan E (2018) Web of Science use in published research and review papers 1997–2017: a selective, dynamic, cross-domain, content-based analysis. Scientometrics 115(1):1–20. https://doi.org/10.1007/s11192-017-2622-5
Liang T-P, Liu Y-H (2018) Research landscape of business intelligence and big data analytics: a bibliometrics study. Expert Syst Appl 111:2–10. https://doi.org/10.1016/j.eswa.2018.05.018
Liao H, Tang M, Luo L, Li C, Chiclana F, Zeng X-J (2018) A bibliometric analysis and visualization of medical big data research. Sustainability 10:166
Makawana PR, Jhaveri RH (2018) A bibliometric analysis of recent research on machine learning for cyber security. In: Hu YC, Tiwari S, Mishra KK, Trivedi MC (eds) Intelligent communication and computational technologies, vol 19. Lecture Notes in Networks and Systems. pp 213–226. https://doi.org/10.1007/978-981-10-5523-2_20
Mäntylä MV, Graziotin D, Kuutila M (2018) The evolution of sentiment analysis—A review of research topics, venues, and top cited papers. Compu Sci Rev 27:16–32. https://doi.org/10.1016/j.cosrev.2017.10.002
Mao M, Li Z, Zhao Z, Zeng L (2018) Bibliometric analysis of the deep learning research status with the data from web of science. In: Tan Y, Shi Y, Tang Q (eds) Data Min Big Data, vol 10943. Springer, Cham
Mosavi A, Salimi M, Ardabili SF, Rabczuk T, Shamshirband S, Varkonyi-Koczy AR (2019) State of the art of machine learning models in energy systems, a systematic review. Energies 12(7):42. https://doi.org/10.3390/en12071301
Nikzad–Khasmakhi N, Balafar MA, Reza Feizi–Derakhshi M (2019) The state-of-the-art in expert recommendation systems. Eng Appl Artif Intell 82:126–147. https://doi.org/10.1016/j.engappai.2019.03.020
Piryani R, Madhavi D, Singh VK (2017) Analytical mapping of opinion mining and sentiment analysis research during 2000–2015. Inf Process Manag 53(1):122–150. https://doi.org/10.1016/j.ipm.2016.07.001
Randhawa K, Wilden R, Hohberger J (2016) A bibliometric review of open innovation: setting a research agenda. J Prod Innov Manag 33(6):750–772. https://doi.org/10.1111/jpim.12312
Sci2 Team (2009) Science of Science (Sci2) Tool. Indiana University and SciTech Strategies. https://sci2.cns.iu.edu. 2019
Saheb T, Saheb M (2019) Analyzing and visualizing knowledge structures of health informatics from 1974 to 2018: a bibliometric and social network analysis. Healthc Inform Res 25(2):61–72. https://doi.org/10.4258/hir.2019.25.2.61
Tran BX, McIntyre RS, Latkin CA, Phan HT, Vu GT, Nguyen HLT, Gwee KK, Ho CSH, Ho RCM (2019) The current research landscape on the artificial intelligence application in the management of depressive disorders: a bibliometric analysis. Int J Environ Res Public Health 16 (12). https://doi.org/10.3390/ijerph16122150
Tran BX, Vu GT, Ha GI, Vuong QH, Ho MT, Vuong TT, La VP, Ho MT, Nghiem KCP, Nguyen HLT, Latkin CA, Tam WWS, Cheung NM, Nguyen HKT, Ho CSH, Ho RCM (2019) Global evolution of research in artificial intelligence in health and medicine: a bibliometric study. J Clin Med 8(3). https://doi.org/10.3390/jcm8030360
van Eck NJ, Waltman L (2010) Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics 84(2):523–538. https://doi.org/10.1007/s11192-009-0146-3
van Eck NJ, Waltman L, Dekker R, van den Berg J (2010) A comparison of two techniques for bibliometric mapping: Multidimensional scaling and VOS. J Am Soc Inform Sci Technol 61(12):2405–2416. https://doi.org/10.1002/asi.21421
Wang XY, Guo JJ, Gu DX, Yang Y, Yang XJ, Zhu KY (2019) Tracking knowledge evolution, hotspots and future directions of emerging technologies in cancers research: a bibliometrics review. J Cancer 10(12):2643–2653. https://doi.org/10.7150/jca.32739
Xu Z, Yu D (2019) A Bibliometrics analysis on big data research (2009–2018). J Data Inf Manag 1(1):3–15. https://doi.org/10.1007/s42488-019-00001-2
Xu ZS, Yu DJ, Wang XZ (2019) A bibliometric overview of International Journal of Machine Learning and Cybernetics between 2010 and 2017. Int J Mach Learn Cybernet 10(9):2375–2387. https://doi.org/10.1007/s13042-018-0875-9
Yeung AWK, Goto TK, Leung WK (2017) A bibliometric review of research trends in neuroimaging. Curr Sci 112(4):725–734
Yu DJ, Xu ZS, Wang XZ (2019) Bibliometric analysis of support vector machines research trend: a case study in China. Int J Mach Learn Cybernet. https://doi.org/10.1007/s13042-019-01028-y
Zimbra D, Abbasi A, Zeng D, Chen HC (2018) The state-of-the-art in twitter sentiment analysis: a review and benchmark evaluation. ACM Trans Manag Inf Syst 9(2):29. https://doi.org/10.1145/3185045
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Lopez-Martinez, R.E., Sierra, G. State of research on natural language processing in Mexico — a bibliometric study. J. of Data, Inf. and Manag. 3, 183–195 (2021). https://doi.org/10.1007/s42488-021-00051-5
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DOI: https://doi.org/10.1007/s42488-021-00051-5