Abstract
Over the years, the retrieval of information from unstructured data has increased significantly. The availability of unstructured data, or to be precise, data in the form of natural language is available in abundance. The main role of a Question and Answering System is to process the natural language query and generate a concise answer to it. There are many works done in recent times, which have given a Question and Answering System that helps in answering factoid or list-type queries asked in natural language, but most of them have made use of structured data. The proposed Natural Language Information Extraction through Question and Answering for Non-Factoid cases (NLIEQA Non-Factoid) accepts data and the query fired by the user in the form of natural language text, processes them, and produces the desirable answer. It avoids training the system and the use of Structured Query Language (SQL) for storage and processing. Another advantage of the model is that it can handle complex queries. The model has a strong use of Named Entity Recognition (NER) for classification and extraction of the answers. It also makes use of Stanford’s Natural Language Tool Kit (NLTK) for tokenizing, tagging, and chunking of the text.
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References
Angeli, G., Nayak, N., Manning, C.: Combining natural logic and shallow reasoning for question answering, pp. 442–452 (2016). https://doi.org/10.18653/v1/P16-1042
Ranjan, P., Rakesh, C.: Question answering system for factoid based question, pp. 221–224 (2016). https://doi.org/10.1109/IC3I.2016.7917964
Lende, S.P., Raghuwanshi, M.M.: Question answering system on education acts using NLP techniques. In: 2016 World Conference on Futuristic Trends in Research and Innovation for Social Welfare (Startup Conclave), Coimbatore, pp. 1–6 (2016). https://doi.org/10.1109/STARTUP.2016.7583963
Wongso, R., Suhartono, D.: A literature review of question answering system using named entity recognition. In: 2016 3rd International Conference on Information Technology, Computer, and Electrical Engineering (ICITACEE), Semarang, pp. 274–277 (2016). https://doi.org/10.1109/ICITACEE.2016.7892454
Ho, H., Mawardi, V., Dharmawan, A.: Question answering system with hidden Markov model speech recognition. In: 2017 3rd International Conference on Science in Information Technology (ICSITech), Bandung, pp. 257–262 (2017). https://doi.org/10.1109/ICSITech.2017.8257121
Ma, R., Zhang, J., Li, M., Chen, L., Gao, J.: Hybrid answer selection model for non-factoid question answering. In: 2017 International Conference on Asian Language Processing (IALP), Singapore, pp. 371–373 (2017). https://doi.org/10.1109/IALP.2017.8300620
Chandurkar, A., Bansal, A.: Information retrieval from a structured knowledgebase. In: 2017 IEEE 11th International Conference on Semantic Computing (ICSC), San Diego, CA, pp. 407–412 (2017). https://doi.org/10.1109/ICSC.2017.95
Mahajan, R.S., Zaver, M.A.: Novel answer ranking approach in question answering system using compositional distributional semantic model. In: 2018 3rd International Conference for Convergence in Technology (I2CT), Pune, pp. 1–5 (2018). https://doi.org/10.1109/I2CT.2018.8529741
Pundge, A., Mahender, C.: Evaluating reasoning in factoid based question answering system by using machine learning approach. In: 2018 3rd International Conference on Communication and Electronics Systems (ICCES), Coimbatore, India, 2018, pp. 821–825 (2018). https://doi.org/10.1109/CESYS.2018.8724085
Yeo, H.: A machine learning based natural language question and answering system for healthcare data search using complex queries. In: 2018 IEEE International Conference on Big Data (Big Data), Seattle, WA, USA, 2018, pp. 2467–2474 (2018). https://doi.org/10.1109/BigData.2018.8622448
Calijorne Soares, M.A., Parreiras, F.S.: A literature review on question answering techniques, paradigms and systems. J. King Saud Univ. Comput. Inf. Sci. (2018). https://doi.org/10.1016/j.jksuci.2018.08.005
Wasim, M., Mahmood, W., Asim, M.N., Khan, M.U.: Multi-label question classification for factoid and list type questions in biomedical question answering. IEEE Access 7, 3882–3896 (2019). https://doi.org/10.1109/access.2018.2887165
Banerjee, P.S., Chakraborty, B., Tripathi, D.: A information retrieval based on question and answering and NER for unstructured information without using SQL. Wirel. Pers. Commun. 108, 1909 (2019). https://doi.org/10.1007/s11277-019-06501-z
Garigliotti, D., Hasibi, F., Balog, K.: Identifying and exploiting target entity type information for ad hoc entity retrieval. Inf. Retr. J. 22, 285 (2019). https://doi.org/10.1007/s10791-018-9346-x
Dimitriadis, D., Tsoumakas, G.: Word embeddings and external resources for answer processing in biomedical factoid question answering. J. Biomed. Inform. (2019). https://doi.org/10.1016/j.jbi.2019.103118
Qu, C., Yang, L., Croft, W.B., Scholer, F., Zhang, Y.: Answer interaction in non-factoid question answering systems. In: Proceedings of the 2019 Conference on Human Information Interaction and Retrieval—CHIIR ’19 (2019). https://doi.org/10.1145/3295750.3298946
Acknowledgements
This publication is an outcome of the R&D work undertaken project under the Visvesvaraya Ph.D. Scheme of Ministry of Electronics and Information Technology, MeitY, Government of India, being implemented by Digital India Corporation. This research work has been done at Research Project Lab of National Institute of Technology (NIT), Durgapur, India. Financial support was received from Visvesvaraya Ph.D. Scheme, Deity, Government of India (Order Number: PHD-MLA/4 (29)/2014_2015 Dated-27/4/2015) to carry out this research work. The authors would like to thank the Department of Computer Science and Engineering, NIT, Durgapur, for academically supporting this research work. The authors would also like to thank the Department of Computer Science and Engineering, Jaypee University of Engineering and Technology, Guna MP.
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Banerjee, P.S., Ghosh, A., Gupta, A., Chakraborty, B. (2021). Natural Language Information Extraction Through Non-Factoid Question and Answering System (NLIEQA Non-Factoid). In: Hassanien, A., Bhatnagar, R., Darwish, A. (eds) Advanced Machine Learning Technologies and Applications. AMLTA 2020. Advances in Intelligent Systems and Computing, vol 1141. Springer, Singapore. https://doi.org/10.1007/978-981-15-3383-9_10
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