[1] Y. Zhu, L. Pang, Y. Lan, H. Shen, and X. Cheng, “Adaptive Information Seeking for Open-Domain Question Answering,” 2021, doi: 10.18653/v1/2021.emnlp-main.293.
[2]F. Zhu, W. Lei, C. Wang, J. Zheng, S. Poria, and T.-S. Chua, “Retrieving and Reading: A Comprehensive Survey on Open-domain Question Answering,” pp. 1–21, 2021, [Online]. Available: http://arxiv.org/abs/2101.00774.
[3]D. Chen, A. Fisch, J. Weston, and A. Bordes, “Reading Wikipedia to answer open-domain questions,” ACL 2017-55th Annu. Meet. Assoc. Comput. Linguist. Proc. Conf. (Long Pap., vol. 1, pp. 1870–1879, 2017, doi: 10.18653/v1/P17-1171.
[4]J. Chen, J. Mao, Y. Liu, F. Zhang, M. Zhang, and S. Ma, “Towards a better understanding of query reformulation behavior in web search,” Web Conf. 2021 -Proc. World Wide Web Conf. WWW 2021, no. 61732008, pp. 743–755, 2021, doi: 10.1145/3442381.3450127.
[5]K. Cao, C. Chen, S. Baltes, C. Treude, and X. Chen, “Automated query reformulation for efficient search based on query logs from stack overflow,” Proc. -Int. Conf. Softw. Eng., no. November, pp. 1273–1285, 2021, doi: 10.1109/ICSE43902.2021.00116.
[6]M. Breja and S. K. Jain, “Why-Type Question to Query Reformulation for Efficient Document Retrieval,” Int. J. Inf. Retr. Res., vol. 12, no. 1, pp. 1–18, 2021, doi: 10.4018/ijirr.289948.
[7]J. Z. Chen, S. Yu, and H. Wang, “Exploring Fluent Query Reformulations with Text-to-Text Transformers and Reinforcement Learning,” vol. 2021, 2020, [Online]. Available: http://arxiv.org/abs/2012.10033.
[8]C. T. Lin, S. P. Ma, and Y. W. Huang, “MSABot: A Chatbot Framework for Assisting in the Development and Operation of Microservice-Based Systems,” Proc. -2020 IEEE/ACM 42nd Int. Conf. Softw. Eng. Work. ICSEW 2020, pp. 36–40, 2020, doi: 10.1145/3387940.3391501.
[9]X. Wang, C. Macdonald, and I. Ounis, “Deep Reinforced Query Reformulation for Information Retrieval,” 2020, [Online]. Available: http://arxiv.org/abs/2007.07987.
[10]H. G. Cavalcante, J. N. Soares, and J. E. B. Maia, “Question Expansionin a Question-Answering System in a Closed-Domain System,” Int. J. Comput. Appl., vol. 183, no. 23, pp. 1–5, 2021, doi: 10.5120/ijca2021921621.
[11]S. Hirsch, I. Guy, A. Nus, A. Dagan, and O. Kurland, “Query Reformulation in E-Commerce Search,” SIGIR 2020 -Proc. 43rd Int. ACM SIGIR Conf. Res. Dev. Inf. Retr., pp. 1319–1328, 2020, doi: 10.1145/3397271.3401065.
[12]A. P. Bhopale and A. Tiwari, “Leveraging Neural Network Phrase Embedding Model for Query Reformulation in Ad-hoc Biomedical Information Retrieval,” Malaysian J. Comput. Sci., vol. 34, no. 2, pp. 151–170, 2021, doi: 10.22452/mjcs.vol34no2.2.
[13]T. Lei, Z. Shi, D. Liu, L. Yang, and F. Zhu, “A novel CNN-based method for question classification in intelligent question answering,” ACM Int. Conf. Proceeding Ser., 2018, doi: 10.1145/3302425.3302483.
[14]P. Panagiotou, G. Kalpakis, T. Tsikrika, S. Vrochidis, and I. Kompatsiaris, “Query Reformulation Based on Word Embeddings: A Comparative Study,” pp. 41–55, 2021, doi: 10.1007/978-3-030-69460-9_3.
[15]N. Stringham and M. Izbicki, “Evaluating Word Embeddings on Low-Resource Languages,” pp. 176–186, 2020, doi: 10.18653/v1/2020.eval4nlp-1.17.
[16]H. K. Azad and A. Deepak, “Query expansion techniques for information retrieval: A survey,” Inf. Process. Manag., vol. 56, no. 5, pp. 1698–1735, 2019, doi: 10.1016/j.ipm.2019.05.009.
[17]F. S. Khan, M. Al Mushabbir, M. S. Irbaz, and M. A. Al Nasim, “End-to-End Natural Language Understanding Pipeline for Bangla Conversational Agent,” 2021, [Online]. Available: http://arxiv.org/abs/2107.05541.
[18]D. Roy, D. Paul, M. Mitra, and U. Garain, “Using Word Embeddings for Automatic Query Expansion,” 2016, [Online]. Available: http://arxiv.org/abs/1606.07608.
[19]F. Diaz, B. Mitra, and N. Craswell, “Query expansion with locally-trained word embeddings,” 54th Annu. Meet. Assoc. Comput. Linguist. ACL 2016 -Long Pap., vol. 1, pp. 367–377, 2016, doi: 10.18653/v1/p16-1035.
[20]S. Kuzi, A. Shtok, and O. Kurland, “Queryexpansion using word embeddings,” Int. Conf. Inf. Knowl. Manag. Proc., vol. 24-28-October-2016, pp. 1929–1932, 2016, doi: 10.1145/2983323.2983876.
[21]T. Mikolov, K. Chen, G. Corrado, and J. Dean, “Efficient estimation of word representations in vector space,” 1st Int. Conf. Learn. Represent. ICLR 2013 -Work. Track Proc., pp. 1–12, 2013.
[22]S. K. Dwivedi and V. Singh, “Research and Reviews in Question Answering System,” Procedia Technol., vol. 10, pp. 417–424, 2013, doi: 10.1016/j.protcy.2013.12.378.
[23]W. Y. C. Meek, “W IKI QA : A Challenge Dataset for Open-Domain Question Answering,” no. September 2015, pp. 2013–2018, 2018, [Online]. Available: http://www.aclweb.org/anthology/D15-1237.
[24]K. N. Lam, N. N. Le, and J. Kalita, “Building a Chatbot on a Closed Domain using RASA,” PervasiveHealth Pervasive Comput. Technol. Healthc., pp. 144–148, 2020, doi: 10.1145/3443279.3443308.
[25]S. Vakulenko, S. Longpre, Z. Tu, and R. Anantha, “Question Rewriting for Conversational Question Answering,” WSDM 2021 -Proc. 14th ACM Int. Conf. Web Search Data Min., pp. 355–363, 2021, doi: 10.1145/3437963.3441748.
[2]F. Zhu, W. Lei, C. Wang, J. Zheng, S. Poria, and T.-S. Chua, “Retrieving and Reading: A Comprehensive Survey on Open-domain Question Answering,” pp. 1–21, 2021, [Online]. Available: http://arxiv.org/abs/2101.00774.
[3]D. Chen, A. Fisch, J. Weston, and A. Bordes, “Reading Wikipedia to answer open-domain questions,” ACL 2017-55th Annu. Meet. Assoc. Comput. Linguist. Proc. Conf. (Long Pap., vol. 1, pp. 1870–1879, 2017, doi: 10.18653/v1/P17-1171.
[4]J. Chen, J. Mao, Y. Liu, F. Zhang, M. Zhang, and S. Ma, “Towards a better understanding of query reformulation behavior in web search,” Web Conf. 2021 -Proc. World Wide Web Conf. WWW 2021, no. 61732008, pp. 743–755, 2021, doi: 10.1145/3442381.3450127.
[5]K. Cao, C. Chen, S. Baltes, C. Treude, and X. Chen, “Automated query reformulation for efficient search based on query logs from stack overflow,” Proc. -Int. Conf. Softw. Eng., no. November, pp. 1273–1285, 2021, doi: 10.1109/ICSE43902.2021.00116.
[6]M. Breja and S. K. Jain, “Why-Type Question to Query Reformulation for Efficient Document Retrieval,” Int. J. Inf. Retr. Res., vol. 12, no. 1, pp. 1–18, 2021, doi: 10.4018/ijirr.289948.
[7]J. Z. Chen, S. Yu, and H. Wang, “Exploring Fluent Query Reformulations with Text-to-Text Transformers and Reinforcement Learning,” vol. 2021, 2020, [Online]. Available: http://arxiv.org/abs/2012.10033.
[8]C. T. Lin, S. P. Ma, and Y. W. Huang, “MSABot: A Chatbot Framework for Assisting in the Development and Operation of Microservice-Based Systems,” Proc. -2020 IEEE/ACM 42nd Int. Conf. Softw. Eng. Work. ICSEW 2020, pp. 36–40, 2020, doi: 10.1145/3387940.3391501.
[9]X. Wang, C. Macdonald, and I. Ounis, “Deep Reinforced Query Reformulation for Information Retrieval,” 2020, [Online]. Available: http://arxiv.org/abs/2007.07987.
[10]H. G. Cavalcante, J. N. Soares, and J. E. B. Maia, “Question Expansionin a Question-Answering System in a Closed-Domain System,” Int. J. Comput. Appl., vol. 183, no. 23, pp. 1–5, 2021, doi: 10.5120/ijca2021921621.
[11]S. Hirsch, I. Guy, A. Nus, A. Dagan, and O. Kurland, “Query Reformulation in E-Commerce Search,” SIGIR 2020 -Proc. 43rd Int. ACM SIGIR Conf. Res. Dev. Inf. Retr., pp. 1319–1328, 2020, doi: 10.1145/3397271.3401065.
[12]A. P. Bhopale and A. Tiwari, “Leveraging Neural Network Phrase Embedding Model for Query Reformulation in Ad-hoc Biomedical Information Retrieval,” Malaysian J. Comput. Sci., vol. 34, no. 2, pp. 151–170, 2021, doi: 10.22452/mjcs.vol34no2.2.
[13]T. Lei, Z. Shi, D. Liu, L. Yang, and F. Zhu, “A novel CNN-based method for question classification in intelligent question answering,” ACM Int. Conf. Proceeding Ser., 2018, doi: 10.1145/3302425.3302483.
[14]P. Panagiotou, G. Kalpakis, T. Tsikrika, S. Vrochidis, and I. Kompatsiaris, “Query Reformulation Based on Word Embeddings: A Comparative Study,” pp. 41–55, 2021, doi: 10.1007/978-3-030-69460-9_3.
[15]N. Stringham and M. Izbicki, “Evaluating Word Embeddings on Low-Resource Languages,” pp. 176–186, 2020, doi: 10.18653/v1/2020.eval4nlp-1.17.
[16]H. K. Azad and A. Deepak, “Query expansion techniques for information retrieval: A survey,” Inf. Process. Manag., vol. 56, no. 5, pp. 1698–1735, 2019, doi: 10.1016/j.ipm.2019.05.009.
[17]F. S. Khan, M. Al Mushabbir, M. S. Irbaz, and M. A. Al Nasim, “End-to-End Natural Language Understanding Pipeline for Bangla Conversational Agent,” 2021, [Online]. Available: http://arxiv.org/abs/2107.05541.
[18]D. Roy, D. Paul, M. Mitra, and U. Garain, “Using Word Embeddings for Automatic Query Expansion,” 2016, [Online]. Available: http://arxiv.org/abs/1606.07608.
[19]F. Diaz, B. Mitra, and N. Craswell, “Query expansion with locally-trained word embeddings,” 54th Annu. Meet. Assoc. Comput. Linguist. ACL 2016 -Long Pap., vol. 1, pp. 367–377, 2016, doi: 10.18653/v1/p16-1035.
[20]S. Kuzi, A. Shtok, and O. Kurland, “Queryexpansion using word embeddings,” Int. Conf. Inf. Knowl. Manag. Proc., vol. 24-28-October-2016, pp. 1929–1932, 2016, doi: 10.1145/2983323.2983876.
[21]T. Mikolov, K. Chen, G. Corrado, and J. Dean, “Efficient estimation of word representations in vector space,” 1st Int. Conf. Learn. Represent. ICLR 2013 -Work. Track Proc., pp. 1–12, 2013.
[22]S. K. Dwivedi and V. Singh, “Research and Reviews in Question Answering System,” Procedia Technol., vol. 10, pp. 417–424, 2013, doi: 10.1016/j.protcy.2013.12.378.
[23]W. Y. C. Meek, “W IKI QA : A Challenge Dataset for Open-Domain Question Answering,” no. September 2015, pp. 2013–2018, 2018, [Online]. Available: http://www.aclweb.org/anthology/D15-1237.
[24]K. N. Lam, N. N. Le, and J. Kalita, “Building a Chatbot on a Closed Domain using RASA,” PervasiveHealth Pervasive Comput. Technol. Healthc., pp. 144–148, 2020, doi: 10.1145/3443279.3443308.
[25]S. Vakulenko, S. Longpre, Z. Tu, and R. Anantha, “Question Rewriting for Conversational Question Answering,” WSDM 2021 -Proc. 14th ACM Int. Conf. Web Search Data Min., pp. 355–363, 2021, doi: 10.1145/3437963.3441748.
- Abstract viewed - 760 times
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Affiliations
Alvi Syahrini Utami
Universitas Sriwijaya
Novi Yusliani
Affiliation not stated
Mastura Diana Marieska
Affiliation not stated
Abdiansah Abdiansah
Universitas Sriwijaya
Query Reformulation for Indonesian Question Answering System Using Word Embedding of Word2Vec
Abstract
Query reformulation is one of the tasks in Information Retrieval (IR), which automatically creates new queries based on previous queries. The main challenge of query reformulation is to create a new query whose meaning or context is similar to the old query. Query reformulation can improve the search for relevant documents for Open-domain Question Answering (OpenQA). The more queries are given to the search system, and the more documents will be generated. We propose a Word Predicted and Substituted (WPS) method for query reformulation using a word embedding word2vec. We tested this method on the Indonesian Question Answering System (IQAS). The test results obtained an E-1 value of 81% and an E-2 value of 274%. These results prove that the query reformulation method with WPS and word-embedding can improve the search for potential IQAS answers.