Solving the Message Classification Problem in Voice Interaction Systems
Keywords:
intelligent systems, case-based reasoning, call routing, classification software, data analysis
Abstract
A method for solving the problem of classifying short-text messages in the form of sentences of customers uttered in talking via the telephone line of organizations is considered. To solve this problem, a classifier was developed, which is based on using a combination of two methods: a description of the subject area in the form of a hierarchy of entities and plausible reasoning based on the case-based reasoning approach, which is actively used in artificial intelligence systems. In solving various problems of artificial intelligence-based analysis of data, these methods have shown a high degree of efficiency, scalability, and independence from data structure. As part of using the case-based reasoning approach in the classifier, it is proposed to modify the TF-IDF (Term Frequency - Inverse Document Frequency) measure of assessing the text content taking into account known information about the distribution of documents by topics. The proposed modification makes it possible to improve the classification quality in comparison with classical measures, since it takes into account the information about the distribution of words not only in a separate document or topic, but in the entire database of cases. Experimental results are presented that confirm the effectiveness of the proposed metric and the developed classifier as applied to classification of customer sentences and providing them with the necessary information depending on the classification result. The developed text classification service prototype is used as part of the voice interaction module with the user in the objective of robotizing the telephone call routing system and making a shift from interaction between the user and system by means of buttons to their interaction through voice.
References
1. Башлыков А.А., Еремеев А.П. Основы конструирования интеллектуальных систем поддержки принятия решений в атомной энергетике. М.: ИНФРА-М, 2018.
2. Еремеев А.П., Варшавский П.Р., Куриленко И.Е. Моделирование временных зависимостей в интеллектуальных системах поддержки принятия решений на основе прецедентов // Information Technol. and Knowledge. 2012. V. 6. No. 3. Pp. 227—239.
3. Kobayashi V.B. е. а. Text Classification for Organizational Researchers: a Tutorial // Organizational Research Methods. 2018. V. 21(3). Pp. 766—799.
4. Куриленко И.Е. Применение рассуждений на основе прецедентов для реализации виртуального сотрудника отдела сопровождения программного обеспечения // Труды 16 Национальной конф. по искусственному интеллекту с междунар. участием. М.: РКП, 2018. Т. 2. С. 238—244.
5. Tiken M. Automatic Speech Recognition System: a Survey Report // Sci. & Technol. J. 2016. V. 4. Pp. 152—155.
6. Arora J., Rishi Sh. Automatic Speech Recognition: a Review. Intern. J. Computer Appl. 2012. V. 60. Pp. 34—44.
7. Еремеев А.П., Варшавский П.Р. Моделирование рассуждений на основе прецедентов в интеллектуальных системах поддержки принятия решений // Искусственный интеллект и принятие решений. 2009. № 2. С. 45—57.
8. Lewis D.D. Naive (Bayes) at Forty: the Independence Assumption in Information Retrieval // Proc. 10 European Conf. Machine Learning. Heidelberg: Springer, 1998. V. 1398. Pp. 4—15.
9. Dumais S., Platt J, Heckerman D., Sahami M. Inductive learning Algoritms and Representations for Text Categorization // Proc. 7 Intern. Conf. Information and Knowledge Management. N.-Y.: ACM Press, 1998. Pp. 148—155.
10. Noormanshah W., Nohuddin P., Zainol Z. Document Categorization Using Decision Tree: Preliminary Study // Intern. J. Eng. and Technol. 2018. V. 7. Pp. 437—440.
11. Healy M. Investigating Text Message Classification Using Case-based Reasoning. Dublin: Dublin Institute of Technology, 2007.
12. Епрев А.С. Автоматическая классификация текстовых документов // Математические структуры и моделирование. 2010. № 1. С. 65—81.
13. Sergienko R., Shany M., Minkerz W., Semenkin E. Topic Categorization Based on Collectives of Term Weighting Methods for Natural Language Call Routing // J. Siberian Federal University. Mathematics and Physics. 2016. V. 9. Pp. 235—245.
14. Korobov M. Morphological Analyzer and Generator for Russian and Ukrainian Languages // Analysis of Images, Social Networks and Texts. Communications in Computer and Information Sci. Springer Intern. Publ., 2015. V. 542. Pp. 320—332.
15. Aamodt A., Plaza E. Case-based Reasoning: Foundational Isuues, Methodological Variations, and System Approaches // AI Communications. 1994. V. 7 (1). Pp. 39—59.
16. Mazyad A., Teytaud F., Fonlupt C. A Comparative Study on Term Weighting Schemes for Text Classification // Proc. Third Intern. Conf. Machine Learning, Optimization and Big Data. Tuscany, 2017.
17. Sparck K.J. A Statistical Interpretation of Term Specificity and its Application in Retrieval // J. Documentation. 1972. V. 28. No. 1. Pp. 11—21.
18. Goutte C., Gaussier E. A Probabilistic Interpretation of Precision, Recall and F-score, with Implication for Evaluation // Proc. 27 European Сonf. Advances in Information Retrieval Research. Berlin: Springer-Verlag, 2005. Pp. 345—359.
19. Lan M., Tan Ch., Su J., Lu Y. Supervised and Traditional Term Weighting Methods for Automatic Text Categorization // IEEE Trans. Pattern Analysis and Machine Intelligence. 2009. V. 31. No. 4. Pp. 721—735.
20. Kurilenko I.E., Nikonov I.E. Virtual Employee Implementation Using Temporal Case-based Reasoning // Enterprise Engineering and Knowledge Managemen: Selected Papers XXII Intern. Conf. Moscow, 2019. V. 2413. Pp. 77—85.
21. Фомин В.В., Флегонтов А.В., Осочкин А.А. Метод частотно-морфологической классификации текстов // Программные продукты и системы. 2017. № 3. C. 478—486.
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Для цитирования: Куриленко И.Е., Никонов И.Е. Решение задачи классификации сообщений в системах голосового взаимодействия // Вестник МЭИ. 2020. № 5. С. 132—139. DOI: 10.24160/1993-6982-2020-5-132-139.
#
1. Bashlykov A.A., Eremeev A.P. Osnovy Konstruirovaniya Intellektual'nykh Sistem Podderzhki Prinyatiya Resheniy v Atomnoy Energetike. M.: INFRA-M, 2018. (in Russian).
2. Eremeev A.P., Varshavskiy P.R., Kurilenko I.E. Modelirovanie Vremennykh Zavisimostey v Intellektual'nykh Sistemakh Podderzhki Prinyatiya Resheniy na Osnove Pretsedentov. Information Technol. and Knowledge. 2012;6;3:227—239. (in Russian).
3. Kobayashi V.B. e. a. Text Classification for Organizational Researchers: a Tutorial. Organizational Research Methods. 2018;21(3):766—799.
4. Kurilenko I.E. Primenenie Rassuzhdeniy na Osnove Pretsedentov dlya realizatsii Virtual'nogo Sotrudnika Otdela Soprovozhdeniya Programmnogo Obespecheniya. Trudy 16 Natsional'noy Konf. po Iskusstvennomu Intellektu s Mezhdunar. Uchastiem. M.: RKP, 2018;2:238—244. (in Russian).
5. Tiken M. Automatic Speech Recognition System: a Survey Report. Sci. & Technol. J. 2016;4:152—155.
6. Arora J., Rishi Sh. Automatic Speech Recognition: a Review. Intern. J. Computer Appl. 2012;60:34—44.
7. Eremeev A.P., Varshavskiy P.R. Modelirovanie Rassuzhdeniy na Osnove Pretsedentov v Intellektual'nykh Sistemakh Podderzhki Prinyatiya Resheniy. Iskusstvennyy Intellekt i Prinyatie Resheniy. 2009;2:45—57. (in Russian).
8. Lewis D.D. Naive (Bayes) at Forty: the Independence Assumption in Information Retrieval. Proc. 10 European Conf. Machine Learning. Heidelberg: Springer, 1998;1398:4—15.
9. Dumais S., Platt J, Heckerman D., Sahami M. Inductive learning Algoritms and Representations for Text Categorization. Proc. 7 Intern. Conf. Information and Knowledge Management. N.-Y.: ACM Press, 1998:148—155.
10. Noormanshah W., Nohuddin P., Zainol Z. Document Categorization Using Decision Tree: Preliminary Study. Intern. J. Eng. and Technol. 2018;7:437—440.
11. Healy M. Investigating Text Message Classification Using Case-based Reasoning. Dublin: Dublin Institute of Technology, 2007.
12. Eprev A.S. Avtomaticheskaya Klassifikatsiya Tekstovykh Dokumentov. Matematicheskie Struktury i Modelirovanie. 2010;1:65—81. (in Russian).
13. Sergienko R., Shany M., Minkerz W., Semenkin E. Topic Categorization Based on Collectives of Term Weighting Methods for Natural Language Call Routing. J. Siberian Federal University. Mathematics and Physics. 2016;9:235—245.
14. Korobov M. Morphological Analyzer and Generator for Russian and Ukrainian Languages. Analysis of Images, Social Networks and Texts. Communications in Computer and Information Sci. Springer Intern. Publ., 2015;542:320—332.
15. Aamodt A., Plaza E. Case-based Reasoning: Foundational Isuues, Methodological Variations, and System Approaches. AI Communications. 1994;7 (1):39—59.
16. Mazyad A., Teytaud F., Fonlupt C. A Comparative Study on Term Weighting Schemes for Text Classification. Proc. Third Intern. Conf. Machine Learning, Optimization and Big Data. Tuscany, 2017.
17. Sparck K.J. A Statistical Interpretation of Term Specificity and its Application in Retrieval. J. Documentation. 1972;28;1:11—21.
18. Goutte C., Gaussier E. A Probabilistic Interpretation of Precision, Recall and F-score, with Implication for Evaluation. Proc. 27 European Sonf. Advances in Information Retrieval Research. Berlin: Springer-Verlag, 2005:345—359.
19. Lan M., Tan Ch., Su J., Lu Y. Supervised and Traditional Term Weighting Methods for Automatic Text Categorization. IEEE Trans. Pattern Analysis and Machine Intelligence. 2009;31;4:721—735.
20. Kurilenko I.E., Nikonov I.E. Virtual Employee Implementation Using Temporal Case-based Rea-soning. Enterprise Engineering and Knowledge Managemen: Selected Papers XXII Intern. Conf. Moscow, 2019;2413:77—85.
21. Fomin V.V., Flegontov A.V., Osochkin A.A. Metod Chastotno-morfologicheskoy Klassifikatsii Tekstov. Programmnye Produkty i Sistemy. 2017;3:478—486. (in Russian).
---
For citation: Kurilenko I.E., Nikonov I.E. Solving the Message Classification Problem in Voice Interaction Systems. Bulletin of MPEI. 2020;5:132—139. (in Russian). DOI: 10.24160/1993-6982-2020-5-132-139.
2. Еремеев А.П., Варшавский П.Р., Куриленко И.Е. Моделирование временных зависимостей в интеллектуальных системах поддержки принятия решений на основе прецедентов // Information Technol. and Knowledge. 2012. V. 6. No. 3. Pp. 227—239.
3. Kobayashi V.B. е. а. Text Classification for Organizational Researchers: a Tutorial // Organizational Research Methods. 2018. V. 21(3). Pp. 766—799.
4. Куриленко И.Е. Применение рассуждений на основе прецедентов для реализации виртуального сотрудника отдела сопровождения программного обеспечения // Труды 16 Национальной конф. по искусственному интеллекту с междунар. участием. М.: РКП, 2018. Т. 2. С. 238—244.
5. Tiken M. Automatic Speech Recognition System: a Survey Report // Sci. & Technol. J. 2016. V. 4. Pp. 152—155.
6. Arora J., Rishi Sh. Automatic Speech Recognition: a Review. Intern. J. Computer Appl. 2012. V. 60. Pp. 34—44.
7. Еремеев А.П., Варшавский П.Р. Моделирование рассуждений на основе прецедентов в интеллектуальных системах поддержки принятия решений // Искусственный интеллект и принятие решений. 2009. № 2. С. 45—57.
8. Lewis D.D. Naive (Bayes) at Forty: the Independence Assumption in Information Retrieval // Proc. 10 European Conf. Machine Learning. Heidelberg: Springer, 1998. V. 1398. Pp. 4—15.
9. Dumais S., Platt J, Heckerman D., Sahami M. Inductive learning Algoritms and Representations for Text Categorization // Proc. 7 Intern. Conf. Information and Knowledge Management. N.-Y.: ACM Press, 1998. Pp. 148—155.
10. Noormanshah W., Nohuddin P., Zainol Z. Document Categorization Using Decision Tree: Preliminary Study // Intern. J. Eng. and Technol. 2018. V. 7. Pp. 437—440.
11. Healy M. Investigating Text Message Classification Using Case-based Reasoning. Dublin: Dublin Institute of Technology, 2007.
12. Епрев А.С. Автоматическая классификация текстовых документов // Математические структуры и моделирование. 2010. № 1. С. 65—81.
13. Sergienko R., Shany M., Minkerz W., Semenkin E. Topic Categorization Based on Collectives of Term Weighting Methods for Natural Language Call Routing // J. Siberian Federal University. Mathematics and Physics. 2016. V. 9. Pp. 235—245.
14. Korobov M. Morphological Analyzer and Generator for Russian and Ukrainian Languages // Analysis of Images, Social Networks and Texts. Communications in Computer and Information Sci. Springer Intern. Publ., 2015. V. 542. Pp. 320—332.
15. Aamodt A., Plaza E. Case-based Reasoning: Foundational Isuues, Methodological Variations, and System Approaches // AI Communications. 1994. V. 7 (1). Pp. 39—59.
16. Mazyad A., Teytaud F., Fonlupt C. A Comparative Study on Term Weighting Schemes for Text Classification // Proc. Third Intern. Conf. Machine Learning, Optimization and Big Data. Tuscany, 2017.
17. Sparck K.J. A Statistical Interpretation of Term Specificity and its Application in Retrieval // J. Documentation. 1972. V. 28. No. 1. Pp. 11—21.
18. Goutte C., Gaussier E. A Probabilistic Interpretation of Precision, Recall and F-score, with Implication for Evaluation // Proc. 27 European Сonf. Advances in Information Retrieval Research. Berlin: Springer-Verlag, 2005. Pp. 345—359.
19. Lan M., Tan Ch., Su J., Lu Y. Supervised and Traditional Term Weighting Methods for Automatic Text Categorization // IEEE Trans. Pattern Analysis and Machine Intelligence. 2009. V. 31. No. 4. Pp. 721—735.
20. Kurilenko I.E., Nikonov I.E. Virtual Employee Implementation Using Temporal Case-based Reasoning // Enterprise Engineering and Knowledge Managemen: Selected Papers XXII Intern. Conf. Moscow, 2019. V. 2413. Pp. 77—85.
21. Фомин В.В., Флегонтов А.В., Осочкин А.А. Метод частотно-морфологической классификации текстов // Программные продукты и системы. 2017. № 3. C. 478—486.
---
Для цитирования: Куриленко И.Е., Никонов И.Е. Решение задачи классификации сообщений в системах голосового взаимодействия // Вестник МЭИ. 2020. № 5. С. 132—139. DOI: 10.24160/1993-6982-2020-5-132-139.
#
1. Bashlykov A.A., Eremeev A.P. Osnovy Konstruirovaniya Intellektual'nykh Sistem Podderzhki Prinyatiya Resheniy v Atomnoy Energetike. M.: INFRA-M, 2018. (in Russian).
2. Eremeev A.P., Varshavskiy P.R., Kurilenko I.E. Modelirovanie Vremennykh Zavisimostey v Intellektual'nykh Sistemakh Podderzhki Prinyatiya Resheniy na Osnove Pretsedentov. Information Technol. and Knowledge. 2012;6;3:227—239. (in Russian).
3. Kobayashi V.B. e. a. Text Classification for Organizational Researchers: a Tutorial. Organizational Research Methods. 2018;21(3):766—799.
4. Kurilenko I.E. Primenenie Rassuzhdeniy na Osnove Pretsedentov dlya realizatsii Virtual'nogo Sotrudnika Otdela Soprovozhdeniya Programmnogo Obespecheniya. Trudy 16 Natsional'noy Konf. po Iskusstvennomu Intellektu s Mezhdunar. Uchastiem. M.: RKP, 2018;2:238—244. (in Russian).
5. Tiken M. Automatic Speech Recognition System: a Survey Report. Sci. & Technol. J. 2016;4:152—155.
6. Arora J., Rishi Sh. Automatic Speech Recognition: a Review. Intern. J. Computer Appl. 2012;60:34—44.
7. Eremeev A.P., Varshavskiy P.R. Modelirovanie Rassuzhdeniy na Osnove Pretsedentov v Intellektual'nykh Sistemakh Podderzhki Prinyatiya Resheniy. Iskusstvennyy Intellekt i Prinyatie Resheniy. 2009;2:45—57. (in Russian).
8. Lewis D.D. Naive (Bayes) at Forty: the Independence Assumption in Information Retrieval. Proc. 10 European Conf. Machine Learning. Heidelberg: Springer, 1998;1398:4—15.
9. Dumais S., Platt J, Heckerman D., Sahami M. Inductive learning Algoritms and Representations for Text Categorization. Proc. 7 Intern. Conf. Information and Knowledge Management. N.-Y.: ACM Press, 1998:148—155.
10. Noormanshah W., Nohuddin P., Zainol Z. Document Categorization Using Decision Tree: Preliminary Study. Intern. J. Eng. and Technol. 2018;7:437—440.
11. Healy M. Investigating Text Message Classification Using Case-based Reasoning. Dublin: Dublin Institute of Technology, 2007.
12. Eprev A.S. Avtomaticheskaya Klassifikatsiya Tekstovykh Dokumentov. Matematicheskie Struktury i Modelirovanie. 2010;1:65—81. (in Russian).
13. Sergienko R., Shany M., Minkerz W., Semenkin E. Topic Categorization Based on Collectives of Term Weighting Methods for Natural Language Call Routing. J. Siberian Federal University. Mathematics and Physics. 2016;9:235—245.
14. Korobov M. Morphological Analyzer and Generator for Russian and Ukrainian Languages. Analysis of Images, Social Networks and Texts. Communications in Computer and Information Sci. Springer Intern. Publ., 2015;542:320—332.
15. Aamodt A., Plaza E. Case-based Reasoning: Foundational Isuues, Methodological Variations, and System Approaches. AI Communications. 1994;7 (1):39—59.
16. Mazyad A., Teytaud F., Fonlupt C. A Comparative Study on Term Weighting Schemes for Text Classification. Proc. Third Intern. Conf. Machine Learning, Optimization and Big Data. Tuscany, 2017.
17. Sparck K.J. A Statistical Interpretation of Term Specificity and its Application in Retrieval. J. Documentation. 1972;28;1:11—21.
18. Goutte C., Gaussier E. A Probabilistic Interpretation of Precision, Recall and F-score, with Implication for Evaluation. Proc. 27 European Sonf. Advances in Information Retrieval Research. Berlin: Springer-Verlag, 2005:345—359.
19. Lan M., Tan Ch., Su J., Lu Y. Supervised and Traditional Term Weighting Methods for Automatic Text Categorization. IEEE Trans. Pattern Analysis and Machine Intelligence. 2009;31;4:721—735.
20. Kurilenko I.E., Nikonov I.E. Virtual Employee Implementation Using Temporal Case-based Rea-soning. Enterprise Engineering and Knowledge Managemen: Selected Papers XXII Intern. Conf. Moscow, 2019;2413:77—85.
21. Fomin V.V., Flegontov A.V., Osochkin A.A. Metod Chastotno-morfologicheskoy Klassifikatsii Tekstov. Programmnye Produkty i Sistemy. 2017;3:478—486. (in Russian).
---
For citation: Kurilenko I.E., Nikonov I.E. Solving the Message Classification Problem in Voice Interaction Systems. Bulletin of MPEI. 2020;5:132—139. (in Russian). DOI: 10.24160/1993-6982-2020-5-132-139.
Published
2012-12-27
Section
Mathematical and Software Support of Computing Machines, Complexes and Computer (05.13.11)
