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NLIIRS: A Question and Answering System for Unstructured Information Using Annotated Text Segment Comparison

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Nanoelectronics, Circuits and Communication Systems (NCCS 2018)


In the current scenario, handling of the unstructured data and extracting information is one of the most crucial aspects. This paper proposes a novel system, Natural Language Information Interpretation and Representation System (NLIIRS) which can accept information in natural language text and can answer to the queries without storing or converting the data into natural language text. Nowadays the presence of available information is large in number in the light of unstructured data. The unstructured information that we get is in the shape of natural languages texts. Protection is needed for the government operative information or any delicate data so that it might be best used when the data can be extricated effectively and effortlessly. Natural Language Information Interpretation and Representation System (NLIIRS) acknowledges the data as characteristic natural language text, process the data and enables the client to recover data by rendering question in natural language. The inquiries subsequently asked are reacted by NLIIRS as expression based answers. The entire frameworks have been composed on Natural Language Tool Kit (NLTK) of Stanford University which helped us to create POS tag, tokenize the information, and shaping the tree structure. The Noval content handling calculation uses the lemmatizer, stemmer and ne chunker to set up the content for data recovery through Q&A. The upside of this framework is that it need not bother with preparing or training. This framework will empower the client to recover any data of his/her decision from the accessible unstructured data.

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  1. D. Lagun, C.-H. Hsieh, D. Webster, V. Navalpakkam, Towards better measurement of attention and satisfaction in mobile search, in Proceedings of the 37th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (2014), pp. 113–122

    Google Scholar 

  2. R. Kadlec, O. Bajgar, J. Kleindienst, From particular to general: a preliminary case study of transfer learning in reading comprehension. Machine Intelligence Workshop (NIPS, 2016)

    Google Scholar 

  3. M. Iyyer, J.L. Boyd-Graber, L.M.B. Claudino, R. Socher, H. Daum´e III. A neural network for factoid question answering over paragraphs. In Empirical Methods in Natural Language Processing (EMNLP). pages 633–644

    Google Scholar 

  4. S. Wang, J. Jiang, A compare-aggregate model for matching text sequences (2016)

    Google Scholar 

  5. F. Yan-hong, Y. Hong, S. Geng, Y. Xun-ran, Domain named entity recognition method based on skip-gram model, in IEEE First International Conference on Electronics Instrumentation & Information Systems (EIIS), China, 22 February 2018 (2018)

    Google Scholar 

  6. A. Mansouri, L.S. Affendey, A. Mamat, R.A. Kadir, Semantically factoid question answering using fuzzy SVM named entity recognition, in IEEE International Symposium on Information Technology, Kuala Lumpur, Malaysia (2008)

    Google Scholar 

  7. P. Ranjan, R.C. Balabantaray, Question answering system for factoid based question, in IEEE 2nd International Conference on Contemporary Computing and Informatics (IC3I), Noida, India (2016)

    Google Scholar 

  8. K.N. Acheampong, Z.-H. Pan, E.-Q. Zhou, X.-Y. Li, Answer triggering of factoid questions: a cognitive approach, in IEEE 13th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP), China (2017)

    Google Scholar 

  9. P.S. Banerjee, B. Chakraborty, D. Tripathi et al., A Information Retrieval Based on Question and Answering and NER for Unstructured Information Without Using SQL, Wireless Personal Communications (Springer, US, 2019).

  10. K. Lei, Y. Deng, B. Zhang, Y. Shen, Open domain question answering with character-level deep learning models, in IEEE 10th International Symposium on Computational Intelligence and Design, China (2017)

    Google Scholar 

  11. M. Tan, B. Xiang, B. Zhou, LSTM-based deep learning models for non-factoid answer selection. Comput. Sci. (2015)

    Google Scholar 

  12. R. Ma, J. Zhang, M. Li, L. Chen, J. Gao, Hybrid answer selection model for non-factoid question answering, in 2017 International Conference on Asian Language Processing (IALP) (2018)

    Google Scholar 

  13. S. Cucerzan, E. Agichtein, Factoid question answering over unstructured and structured web content. Microsoft Research, One Microsoft Way, Redmond, WA 98052 (2011)

    Google Scholar 

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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, Govt. 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 & Technology, Guna MP.

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Correspondence to Gupta Hardik .

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Sarathy, B.P., Baisakhi, C., Deepak, T., Hardik, G., Kumar, S.S. (2020). NLIIRS: A Question and Answering System for Unstructured Information Using Annotated Text Segment Comparison. In: Nath, V., Mandal, J. (eds) Nanoelectronics, Circuits and Communication Systems. NCCS 2018. Lecture Notes in Electrical Engineering, vol 642. Springer, Singapore.

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