Digital Texts as a Teaching Alternative of the Mother Language
DOI:
https://doi.org/10.19053/0121053X.n39.2022.13436Keywords:
computer linguistics, machine translation, corpus linguistics, relations extractionAbstract
Machine translation (MT) is used to obtain annotated corpus of English corpus which can be applicable to different natural language processing (NLP) tasks. Considering that there are more resources or data sets for training NLP models in
English language, this paper explores the application of MT to automate NLP tasks in
Spanish. Thus, the article describes a dataset for the extraction of generic relations (reACE) and the construction of a semantic extraction model of relations in Spanish (ER), based on the set of samples translated from English to Spanish. The results show that for the MT task it is necessary to implement a corpus preediting process in English to avoid translation and post-editing errors and maintain the original corpus annotations. The ER models in Spanish achieve measures of accuracy, completeness, and F-value comparable to those obtained by the model in the English language, which suggests that machine translation is a useful tool to perform NLP tasks in the Spanish language.
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References
Ananthram, A., Allaway, E., & McKeown, K. (2020). Event Guided Denoising for
Multilingual Relation Learning. arXiv preprint: arXiv:2012.02721. https://
doi.org/10.18653/v1/2020.coling-main.131
Anastasopoulos, A. (2019). An Analysis of Source-Side Grammatical Errors in NMT. arXiv preprint: arXiv:1905.10024. DOI: https://doi.org/10.18653/v1/W19-4822
Bach, N., & Sameer, B. (2007). A Survey on Relation Extraction. Language Technologies Institute, Carnegie Mellon University 178. https://doi.org/10.1007/978-981-10-7359-5_6 DOI: https://doi.org/10.1007/978-981-10-7359-5_6
Bahr, R. H., Lebby, S., & Wilkinson, L. C. (2020). Spelling Error Analysis of Written Summaries in an Academic Register by Students with Specific Learning Disabilities: Phonological, Orthographic, and Morphological Influences. Reading and Writing, 33(1), 121-142. https://doi.org/10.1007/s11145- DOI: https://doi.org/10.1007/s11145-019-09977-0
-09977-0
Belinkov, Y., & Glass, J. (2019). Analysis Methods in Neural Language Processing: A Survey. Transactions of the Association for Computational Linguistics, 7, 49-72. https://doi.org/10.1162/tacl_a_00254 Carrino, C. P., Costa-Jussà, M. R., & Fonollosa, J. A. (2020). Automatic Spanish Translation of SQuAD Dataset for Multi-lingual Question Answering. In Proceedings of the 12th Language Resources and Evaluation Conference DOI: https://doi.org/10.1162/tacl_a_00254
(5515-5523).
Castillo, M. N. (2020). Corpus Básico del Español de Chile ©: metodología de procesamiento y análisis. Lexis, 44(2), 483-523. https://doi.org/10.18800/lexis.202002.004 DOI: https://doi.org/10.18800/lexis.202002.004
Cheng, Y. (2019). Neural Machine Translation. In Joint Training for Neural Machine Translation (1-10). Springer. https://doi.org/10.1007/978-981-32- 9748-7_1 DOI: https://doi.org/10.1007/978-981-32-9748-7_1
Collantes, C., Mallo, J., Parra, C., Quiñones, H. & Serrano, R. (2018). Pásate al lado oscuro: ventajas de la traducción automática para el traductor profesional. La Linterna del Traductor, 17, 33-39.
Gamallo, P., & García, M. (2017). LinguaKit: Uma ferramenta multilingue para a análise linguística e a extração de informação. Linguamática, 9(1), 19-28. https://doi.org/10.21814/lm.9.1.243 DOI: https://doi.org/10.21814/lm.9.1.243
Guan, H., Li, J., Xu, H., & Devarakonda, M. (2020). Robustly Pre-trained Neural Model for Direct Temporal Relation Extraction. arXiv preprint: arXiv:2004.06216. https://doi.org/10.1109/ICHI52183.2021.00090 DOI: https://doi.org/10.1109/ICHI52183.2021.00090
Gurulingappa, H., Rajput, A. M., Roberts, A., Fluck, J., Hofmann-Apitius, M., & Toldo, L. (2012). Development of a Benchmark Corpus to Support the Automatic Extraction of Drug-Related Adverse Effects from Medical Case Reports. Journal of Biomedical Informatics, 45(5), 885–892. https://doi.org/10.1016/j.jbi.2012.04.008 DOI: https://doi.org/10.1016/j.jbi.2012.04.008
Hachey, B., Grover, C., & Tobin, R. (2012). Datasets for Generic Relation Extraction. Natural Language Engineering, 18(1), 21–59. http://dx.doi.org/10.1017/ S1351324911000106 DOI: https://doi.org/10.1017/S1351324911000106
Haque, R., Hasanuzzaman, M., & Way, A. (2020). Analysing Terminology Translation Errors in Statistical and Neural Machine Translation. Machine Translation, 34(2), 149-195. https://doi.org/10.1007/s10590-020 09251-z DOI: https://doi.org/10.1007/s10590-020-09251-z
Hidalgo-Ternero, C. M. (2021). Google Translate vs. DeepL. MonTI. Monografías de Traducción e Interpretación, 154-177. DOI: https://doi.org/10.6035/MonTI.2020.ne6.5
Kramer, O. (2016). Scikit-learn. In Machine learning for evolution strategies. Studies in Big Data, vol 20 (pp. 45-53). Springer, Cham. https://doi. org/10.1007/978-3-319-33383-0_5 DOI: https://doi.org/10.1007/978-3-319-33383-0_5
Kumar, S. (2017). A Survey of Deep Learning Methods for Relation Extraction. arXiv preprint: arXiv:1705.03645.
Lin, Y., Liu, Z., & Sun, M. (2017). Neural Relation Extraction with Multi-Lingual Attention. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 34–43. Association for Computational Linguistics. http://dx.doi.org/10.18653/v1/P17-1004 DOI: https://doi.org/10.18653/v1/P17-1004
Mesquita, F., Schmidek, J., & Barbosa, D. (2013). Effectiveness and Efficiency of Open Relation Extraction. In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, 447-457. Association for Computational Linguistics.
Mikelenić, B., & Tadić, M. (2020). Building the Spanish-Croatian Parallel Corpus. In Proceedings of the 12th Language Resources and Evaluation Conference,3932-3936. European Language Resources Association
Mitchell, A., Strassel, S., Huang, S., & Zakhary, R. (2005). Ace 2004 Multilingual Training Corpus. Linguistic Data Consortium, Philadelphia, 1, 1-1.
Nasar, Z., Jaffry, S. W., & Malik, M. K. (2021). Named Entity Recognition and Relation Extraction: State-of-the-Art. ACM Computing Surveys (CSUR), 54(1), 1-39. https://doi.org/10.1145/3445965 DOI: https://doi.org/10.1145/3445965
Ni, J., & Florian, R. (2019). Neural Cross-Lingual Relation Extraction Based on Bilingual Word Embedding Mapping. arXiv preprint: arXiv:1911.00069. https://doi. org/10.18653/v1/D19-1038 DOI: https://doi.org/10.18653/v1/D19-1038
Pastor, G. C. (2018). Laughing One’s Head Off in Spanish Subtitles: A Corpus-Based Study on Diatopic Variation and Its Consequences for Translation1. Fraseología, Diatopía y Traducción/Phraseology, Diatopic Variation and Translation, 17, 32. https://doi.org/10.1075/ivitra.17.03co DOI: https://doi.org/10.1075/ivitra.17.03cor
Pawar, S., Palshikar, G. K., & Bhattacharyya, P. (2017). Relation Extraction: A Survey. arXiv preprint: arXiv:1712.05191.
Popović, M. (2020). Relations Between Comprehensibility and Adequacy Errors in Machine Translation Output. In Proceedings of the 24th Conference on Computational Natural Language Learning, (pp. 256-264). Association for Computational Linguistics. https://doi.org/10.18653/v1/2020. conll-1.19 DOI: https://doi.org/10.18653/v1/2020.conll-1.19
Pyysalo, S., Ginter, F., Heimonen, J., Björne, J., Boberg, J., Järvinen, J., & Salakoski, T. (2007). BioInfer: A Corpus for Information Extraction in the Biomedical Domain. BMC Bioinformatics, 8(1), 50. https://doi.org/10.1186/1471- 2105-8-50 DOI: https://doi.org/10.1186/1471-2105-8-50
Rodrigues, J., & Branco, A. (2020). Argument Identification in a Language Without Labeled Data. In International Conference on Computational Processing of the Portuguese Language, (pp. 335-345). https://doi.org/10.1007/978-3- 030-41505-1_32 DOI: https://doi.org/10.1007/978-3-030-41505-1_32
Sánchez, A. (2010). Traducción automática, corpus lingüísticos y desambiguación automática de los significados de las palabras. En R. Rabadán, M. Fernández & T. Guzmán (coords.), Lengua, traducción, recepción: en honor de Julio César Santoyo, vol. 1 (pp. 555-587). Universidad de León, Área de Publicaciones. Smirnova, A., & Cudré-Mauroux, P. (2018). Relation Extraction Using Distant
Supervision: A Survey. ACM Computing Surveys (CSUR), 51(5), 1-35. https:// doi.org/10.1145/3241741
Torres, J. P., De Piñérez Reyes, R. G., & Bucheli, V. A. (2018). Support Vector Machines for Semantic Relation Extraction in Spanish Language. Colombian Conference on Computing, 326-337. https://doi.org/10.1007/978-3-319-98998- 3_26 DOI: https://doi.org/10.1007/978-3-319-98998-3_26
Verga, P., Belanger, D., Strubell, E., Roth, B., & McCallum, A. (2015). Multilingual Relation Extraction Using Compositional Universal Schema. arXiv preprint: arXiv:1511.06396. https://doi.org/10.18653/v1/N16-1103 DOI: https://doi.org/10.18653/v1/N16-1103
Virmani, C., Pillai, A., & Juneja, D. (2017). Extracting Information from Social Networks Using NLP. International Journal of Computational Intelligence Research, 13(4), 621-630.
Walker, C., Strassel, S., Medero, J., & Maeda, K. (2006). ACE 2005 Multilingual Training Corpus. Linguistic Data Consortium. https://doi.org/10.35111/ mwxc-vh88
Wu, Y., Schuster, M., Chen, Z., Le, Q., Norouzi, M., Machery, W., Krikun, M. et al. (2016). Google’s Neural Machine Translation System: Bridging the Gap Between Human and Machine Translation. arXiv preprint: arXiv:1609.08144.
Yamada, M. (2019). The Impact of Google Neural Machine Translation on Post-Editing by Student Translators. The Journal of Specialised Translation, 31, 87-106.
Zelenko, D., Chinatsu, A., and Anthony, R. (2003, Feb.). Kernel Methods for Relation Extraction. Journal of Machine Learning Research, 3, 1083-1106. https:// dl.acm.org/doi/10.3115/1118693.1118703
Zhang, Q., Mengdong C., and Lianzhong, L. (2017). A Review on Entity Relation Extraction. In 2017 Second International Conference on Mechanical, Control and Computer Engineering (ICMCCE). IEEE. https://doi.org/10.1109/ ICMCCE.2017.14 DOI: https://doi.org/10.1109/ICMCCE.2017.14
Zhila, A., & Gelbukh, A. (2013). Comparison of Open Information Extraction for Spanish and English. Computational Linguistics and Intellectual Technologies, 12(1), 794-802.
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