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<article article-type="research-article" dtd-version="1.3" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xml:lang="ru"><front><journal-meta><journal-id journal-id-type="publisher-id">ntv</journal-id><journal-title-group><journal-title xml:lang="ru">Научно-технический вестник информационных технологий, механики и оптики</journal-title><trans-title-group xml:lang="en"><trans-title>Scientific and Technical Journal of Information Technologies, Mechanics and Optics</trans-title></trans-title-group></journal-title-group><issn pub-type="ppub">2226-1494</issn><issn pub-type="epub">2500-0373</issn><publisher><publisher-name>Университет ИТМО</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.17586/2226-1494-2022-22-3-594-599</article-id><article-id custom-type="elpub" pub-id-type="custom">ntv-277</article-id><article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="ru"><subject>КОМПЬЮТЕРНЫЕ СИСТЕМЫ И ИНФОРМАЦИОННЫЕ ТЕХНОЛОГИИ</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="en"><subject>COMPUTER SCIENCE</subject></subj-group></article-categories><title-group><article-title>Веб-приложение для быстрой оценки субъективных ответов с использованием обработки естественного языка</article-title><trans-title-group xml:lang="en"><trans-title>Web app for quick evaluation of subjective answers using natural language processing</trans-title></trans-title-group></title-group><contrib-group><contrib contrib-type="author" corresp="yes"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0001-7048-7289</contrib-id><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Минакши Анураг</surname><given-names>Т.</given-names></name><name name-style="western" xml:lang="en"><surname>Meenakshi Anurag</surname><given-names>Th.</given-names></name></name-alternatives><bio xml:lang="ru"><p>Минакши Анураг Талор — PhD, доцент</p><p>Пуна, 411001</p></bio><bio xml:lang="en"><p>Thalor Meenakshi Anurag — PhD, Associate Professor</p><p>Pune, 411001</p></bio><email xlink:type="simple">meenakshi.thalor@aissmsioit.org</email><xref ref-type="aff" rid="aff-1"/></contrib><contrib contrib-type="author" corresp="yes"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0002-3252-9983</contrib-id><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Прадип Б.</surname><given-names>М.</given-names></name><name name-style="western" xml:lang="en"><surname>Pradeep B.</surname><given-names>M.</given-names></name></name-alternatives><bio xml:lang="ru"><p>Прадип Б. Мане — PhD, директор</p><p>Пуна, 411001</p></bio><bio xml:lang="en"><p>Mane Pradeep B. — PhD, Principal</p><p>Pune, 411001</p></bio><email xlink:type="simple">pbmane6829@gmail.com</email><xref ref-type="aff" rid="aff-1"/></contrib><contrib contrib-type="author" corresp="yes"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0002-1667-4814</contrib-id><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Вишака</surname><given-names>М.</given-names></name><name name-style="western" xml:lang="en"><surname>Vishaka</surname><given-names>M.</given-names></name></name-alternatives><bio xml:lang="ru"><p>Вишака Мандж — бакалавр, студент</p><p>Пуна, 411001</p></bio><bio xml:lang="en"><p>Mandge Vishaka — B.E., Student</p><p>Pune, 411001</p></bio><email xlink:type="simple">mandge1999@gmail.com</email><xref ref-type="aff" rid="aff-1"/></contrib></contrib-group><aff-alternatives id="aff-1"><aff xml:lang="ru"><institution>AISSMS Институт информационных технологий</institution><country>Индия</country></aff><aff xml:lang="en"><institution>AISSMS Institute of Information Technology</institution><country>India</country></aff></aff-alternatives><pub-date pub-type="collection"><year>2022</year></pub-date><pub-date pub-type="epub"><day>17</day><month>12</month><year>2024</year></pub-date><volume>22</volume><issue>3</issue><fpage>594</fpage><lpage>599</lpage><permissions><copyright-statement>Copyright &amp;#x00A9; Минакши Анураг Т., Прадип Б. М., Вишака М., 2024</copyright-statement><copyright-year>2024</copyright-year><copyright-holder xml:lang="ru">Минакши Анураг Т., Прадип Б. М., Вишака М.</copyright-holder><copyright-holder xml:lang="en">Meenakshi Anurag T., Pradeep B. M., Vishaka M.</copyright-holder><license xml:lang="ru" license-type="creative-commons-attribution" xlink:href="https://creativecommons.org/licenses/by/4.0/" xlink:type="simple"><license-p>Данная работа распространяется под лицензией Creative Commons Attribution 4.0.</license-p></license><license xml:lang="en" license-type="creative-commons-attribution" xlink:href="https://creativecommons.org/licenses/by/4.0/" xlink:type="simple"><license-p>This work is licensed under a Creative Commons Attribution 4.0 License.</license-p></license></permissions><self-uri xlink:href="https://ntv.elpub.ru/jour/article/view/277">https://ntv.elpub.ru/jour/article/view/277</self-uri><abstract><p>В современном цифровом пространстве развитие образования связано с продвижением компьютерных технологий. Происходит цифровизация образования: проводятся онлайн-классы, онлайн-экзамены и т. д. В период проведения экзамена учащиеся оцениваются педагогом по ответам на заданные вопросы. На настоящий момент доступно множество инструментов оценки успеваемости учащегося с использованием вопросов с несколькими вариантами ответов. Такой подход обеспечивает мгновенную оценку, однако инструментов, дающих возможность оценки субъективных ответов учащихся еще недостаточно. В работе представлено веб-приложение для решения этой проблемы. Приложение позволяет автоматизировать процесс проверки субъективных ответов и генерировать результаты с применением методов обработки естественного языка, таких как семантическое сопоставление ключевых слов, лексический анализ и косинусное сходство. Экспериментальные результаты показали, что ответы, оцененные педагогами, и предложенная система дают близкие результаты. Полученная точность ответов составила 97 %. Представленная система не только сокращает рабочее время, но и позволяет устранить традиционный метод проведения исключительно субъективных экзаменов с использованием бумажных документов. В результате сокращается время получения результатов и время проверки. Уменьшается утечка информации и повышается объективность оценки. </p></abstract><trans-abstract xml:lang="en"><p>In current digital climate, education sector is evolving as the computer technology advances. Education is being digitized: online classes, online examination methods are conducted, etc. During examination, students are assessed by their answers having given for the question set by a teacher. Today many tools are available to assess the performance of a student using multi choice questions tools which provide instant evaluation, but there are available very limited and operational tools where subjective type answer of students are evaluated. This paper presents a web-based application to address this challenge. It automates the process of subjective answers checking and generates results through using natural language processing methods, like keyword matching semantic, lexical analysis and cosine similarity. Experiments show that appreciated by the teacher result and the system estimation does not have much difference which signifies that the system evaluates answers with a 97 % accuracy. The presented system not only reduces manpower but also eliminates the traditional method of conducting exclusively subjective exams using paper documents. It also eliminates the delays in the paper checking, result generation process. The cases of information leak are being reduced and the objectivity of the assessment is being increased.</p></trans-abstract><kwd-group xml:lang="ru"><kwd>косинусное сходство</kwd><kwd>извлечение информации</kwd><kwd>сопоставление ключевых слов</kwd><kwd>обработка естественного языка</kwd></kwd-group><kwd-group xml:lang="en"><kwd>cosine similarity</kwd><kwd>information extraction</kwd><kwd>keyword matching</kwd><kwd>natural language processing</kwd></kwd-group></article-meta></front><back><ref-list><title>References</title><ref id="cit1"><label>1</label><citation-alternatives><mixed-citation xml:lang="ru">Lakshmi V., Ramesh V. Evaluating students’ descriptive answers using natural language processing and artificial neural networks. International Journal of Creative Research Thoughts — IJCRT, 2017, vol. 5, no. 4, pp. 3168–3173.</mixed-citation><mixed-citation xml:lang="en">Lakshmi V., Ramesh V. Evaluating students’ descriptive answers using natural language processing and artificial neural networks. International Journal of Creative Research Thoughts — IJCRT, 2017, vol. 5, no. 4, pp. 3168–3173.</mixed-citation></citation-alternatives></ref><ref id="cit2"><label>2</label><citation-alternatives><mixed-citation xml:lang="ru">Patil S.M., Patil S. Evaluating student descriptive answers using natural language processing. International Journal of Engineering Research &amp; Technology (IJERT), 2014, vol. 3, no. 3, pp. 1716–1718.</mixed-citation><mixed-citation xml:lang="en">Patil S.M., Patil S. Evaluating student descriptive answers using natural language processing. International Journal of Engineering Research &amp; Technology (IJERT), 2014, vol. 3, no. 3, pp. 1716–1718.</mixed-citation></citation-alternatives></ref><ref id="cit3"><label>3</label><citation-alternatives><mixed-citation xml:lang="ru">Yin W., Kann K., Yu M., Schütze H. Comparative study of CNN and RNN for natural language processing. ArXiv, 2017, arXiv:1702.01923. https://doi.org/10.48550/arXiv.1702.01923</mixed-citation><mixed-citation xml:lang="en">Yin W., Kann K., Yu M., Schütze H. Comparative study of CNN and RNN for natural language processing. ArXiv, 2017, arXiv:1702.01923. https://doi.org/10.48550/arXiv.1702.01923</mixed-citation></citation-alternatives></ref><ref id="cit4"><label>4</label><citation-alternatives><mixed-citation xml:lang="ru">Kate A., Kamble S., Bodkhe A., Joshi M. Conversion of Natural Language Query to SQL Query. Proc. of the 2nd International conference on Electronics, Communication and Aerospace Technology (ICECA), 2018, pp. 488–491. https://doi.org/10.1109/ICECA.2018.8474639</mixed-citation><mixed-citation xml:lang="en">Kate A., Kamble S., Bodkhe A., Joshi M. Conversion of Natural Language Query to SQL Query. Proc. of the 2nd International conference on Electronics, Communication and Aerospace Technology (ICECA), 2018, pp. 488–491. https://doi.org/10.1109/ICECA.2018.8474639</mixed-citation></citation-alternatives></ref><ref id="cit5"><label>5</label><citation-alternatives><mixed-citation xml:lang="ru">Thangarasu S., Sasikala D. Extracting knowledge from XML document using tree-based association rules. Proc. of the International Conference on Intelligent Computing Applications (ICICA), 2014, pp. 134–137. https://doi.org/10.1109/ICICA.2014.37</mixed-citation><mixed-citation xml:lang="en">Thangarasu S., Sasikala D. Extracting knowledge from XML document using tree-based association rules. Proc. of the International Conference on Intelligent Computing Applications (ICICA), 2014, pp. 134–137. https://doi.org/10.1109/ICICA.2014.37</mixed-citation></citation-alternatives></ref><ref id="cit6"><label>6</label><citation-alternatives><mixed-citation xml:lang="ru">Mati D.N., Ajdari J., Raufi B., Hamiti M., Selimi B. A Systematic mapping study of language features identification from large text collection. Proc. of the 8th Mediterranean Conference on Embedded Computing (MECO), 2019, pp. 8760042. https://doi.org/10.1109/MECO.2019.8760042</mixed-citation><mixed-citation xml:lang="en">Mati D.N., Ajdari J., Raufi B., Hamiti M., Selimi B. A Systematic mapping study of language features identification from large text collection. Proc. of the 8th Mediterranean Conference on Embedded Computing (MECO), 2019, pp. 8760042. https://doi.org/10.1109/MECO.2019.8760042</mixed-citation></citation-alternatives></ref><ref id="cit7"><label>7</label><citation-alternatives><mixed-citation xml:lang="ru">Cai J., Li J., Li W., Wang J. Deeplearning model used in text classification. Proc of the 15th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP), 2018, pp. 123–126. https://doi.org/10.1109/ICCWAMTIP.2018.8632592</mixed-citation><mixed-citation xml:lang="en">Cai J., Li J., Li W., Wang J. Deeplearning model used in text classification. Proc of the 15th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP), 2018, pp. 123–126. https://doi.org/10.1109/ICCWAMTIP.2018.8632592</mixed-citation></citation-alternatives></ref><ref id="cit8"><label>8</label><citation-alternatives><mixed-citation xml:lang="ru">Tan M., Santos C., Xiang B., Zhou B. Improved representation learning for question answer matching. Proc. of the 54th Annual Meeting of the Association for Computational Linguistics (ACL), 2016, pp. 464–473. https://doi.org/10.18653/v1/P16-1044</mixed-citation><mixed-citation xml:lang="en">Tan M., Santos C., Xiang B., Zhou B. Improved representation learning for question answer matching. Proc. of the 54th Annual Meeting of the Association for Computational Linguistics (ACL), 2016, pp. 464–473. https://doi.org/10.18653/v1/P16-1044</mixed-citation></citation-alternatives></ref><ref id="cit9"><label>9</label><citation-alternatives><mixed-citation xml:lang="ru">Lan Y., Wang S., Jiang J. Knowledge base question answering with a matching-aggregation model and question-specific contextual relations. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2019, vol. 27, no. 10, pp. 1629–1638. https://doi.org/10.1109/TASLP.2019.2926125</mixed-citation><mixed-citation xml:lang="en">Lan Y., Wang S., Jiang J. Knowledge base question answering with a matching-aggregation model and question-specific contextual relations. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2019, vol. 27, no. 10, pp. 1629–1638. https://doi.org/10.1109/TASLP.2019.2926125</mixed-citation></citation-alternatives></ref><ref id="cit10"><label>10</label><citation-alternatives><mixed-citation xml:lang="ru">Grappy A., Grau B., Falco M-H., Ligozat A-L., Robba I., Vilnat A. Selecting answers to questions from Web documents by a robust validation process. Proc. of the IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology, 2011, pp. 55–62. https://doi.org/10.1109/WI-IAT.2011.210</mixed-citation><mixed-citation xml:lang="en">Grappy A., Grau B., Falco M-H., Ligozat A-L., Robba I., Vilnat A. Selecting answers to questions from Web documents by a robust validation process. Proc. of the IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology, 2011, pp. 55–62. https://doi.org/10.1109/WI-IAT.2011.210</mixed-citation></citation-alternatives></ref><ref id="cit11"><label>11</label><citation-alternatives><mixed-citation xml:lang="ru">Lan Y., Wang S., Jiang J. Knowledge base question answering with a matching-aggregation model and question specific contextual relations. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2019, vol. 27, no. 10, pp. 1629–1638. https://doi.org/10.1109/TASLP.2019.2926125</mixed-citation><mixed-citation xml:lang="en">Lan Y., Wang S., Jiang J. Knowledge base question answering with a matching-aggregation model and question specific contextual relations. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2019, vol. 27, no. 10, pp. 1629–1638. https://doi.org/10.1109/TASLP.2019.2926125</mixed-citation></citation-alternatives></ref><ref id="cit12"><label>12</label><citation-alternatives><mixed-citation xml:lang="ru">Zhang W., Ming Z., Zhang Y., Liu T., Chua T. Capturing the semantics of key phrases using multiple languages for question retrieval. IEEE Transactions on Knowledge and Data Engineering, 2016, vol. 28, no. 4, pp. 888–900. https://doi.org/10.1109/TKDE.2015.2502944</mixed-citation><mixed-citation xml:lang="en">Zhang W., Ming Z., Zhang Y., Liu T., Chua T. Capturing the semantics of key phrases using multiple languages for question retrieval. IEEE Transactions on Knowledge and Data Engineering, 2016, vol. 28, no. 4, pp. 888–900. https://doi.org/10.1109/TKDE.2015.2502944</mixed-citation></citation-alternatives></ref><ref id="cit13"><label>13</label><citation-alternatives><mixed-citation xml:lang="ru">Pakray P., Pal S., Bandyopadhyay S., Gelbukh A. Automatic answer validation system on English language. Proc. of the 3rd International Conference on Advanced Computer Theory and Engineering, 2010, pp. V6329–V6333. https://doi.org/10.1109/ICACTE.2010.5579166</mixed-citation><mixed-citation xml:lang="en">Pakray P., Pal S., Bandyopadhyay S., Gelbukh A. Automatic answer validation system on English language. Proc. of the 3rd International Conference on Advanced Computer Theory and Engineering, 2010, pp. V6329–V6333. https://doi.org/10.1109/ICACTE.2010.5579166</mixed-citation></citation-alternatives></ref><ref id="cit14"><label>14</label><citation-alternatives><mixed-citation xml:lang="ru">Thalor M.A. A descriptive answer evaluation system using cosine similarity technique. Proc. of the 4th International Conference on Communication information and Computing Technology (ICCICT), 2021. https://doi.org/10.1109/ICCICT50803.2021.9510170</mixed-citation><mixed-citation xml:lang="en">Thalor M.A. A descriptive answer evaluation system using cosine similarity technique. Proc. of the 4th International Conference on Communication information and Computing Technology (ICCICT), 2021. https://doi.org/10.1109/ICCICT50803.2021.9510170</mixed-citation></citation-alternatives></ref><ref id="cit15"><label>15</label><citation-alternatives><mixed-citation xml:lang="ru">Mandge V.A., Thalor M.A. Revolutionize cosine answer matching technique for question answering system. Proc. of the International Conference on Emerging Smart Computing and Informatics (ESCI), 2021, pp. 335–339. https://doi.org/10.1109/ESCI50559.2021.9396864</mixed-citation><mixed-citation xml:lang="en">Mandge V.A., Thalor M.A. Revolutionize cosine answer matching technique for question answering system. Proc. of the International Conference on Emerging Smart Computing and Informatics (ESCI), 2021, pp. 335–339. https://doi.org/10.1109/ESCI50559.2021.9396864</mixed-citation></citation-alternatives></ref></ref-list><fn-group><fn fn-type="conflict"><p>The authors declare that there are no conflicts of interest present.</p></fn></fn-group></back></article>
