Abstract
Researches on question answering systems has been attracting significant research attention in recent years with the explosive data growth and breakthroughs in machine learning paradigm. Answer selection in question answering segment is always considered as a challenging task in natural language processing domain. The major difficulty detected here is that it not only needs the consideration of semantic matching between question answer pairs but also requires a serious modeling of contextual factors. The system aims to use deep learning technique to generate the expected answer. Sequential ensemble approach is deployed in the proposed model, where it categorically boosts the prediction of LSTM and memory network to increase the system accuracy. The proposed model shows a 50% increase in accuracy when compared to individual systems with a few number of epochs. The proposed system reduces the training time and boosts the system-level accuracy.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Abishek K, Hariharan BK, Valliyammai C (2019) An enhanced deep learning model for duplicate question pairs recognition. In: Soft computing in data analytics. Springer, Singapore, pp 769–777
Bae K, Ko Y (2019) Efficient question classification and retrieval using category information and word embedding on cQA services. Springer J Intell Inf Syst 53:27–49
Razzaghnoori M, Sajedi H, Jazani IK (2018) Question classification in Persian using word vectors and frequencies. ACM J Cogn Syst Res 47(C):16–27
Hong R et al (2019) Learning to compose and reason with language tree structures for visual grounding. IEEE Trans Pattern Anal Mach Intell
Liu B, Zhang T, Niu D, Lin J, Lai K, Xu Y (2018) Matching long text documents via graph convolutional networks. arXiv:1802.07459v1 [cs.CL] 21 2018
Ullah A, Xiao H, Barker T (2019) A study into the usability and security implications of text and image based challenge questions in the context of online examination. Springer Educ Inf Technol 24(1):13–39
Yue C, Cao H, Xiong K, Cui A, Qin H, Li M (2017) Enhanced question understanding with dynamic memory networks for textual question answering. Expert Syst Appl 1(80):39–45
Manning C, Text-based question answering systems, p 7. http://web.stanford.edu/class/cs224n/handouts/cs224n-QA-2013.pdf
Quarteroni S (2007) A chatbot-based interactive question answering system. In: 11th Workshop on the semantics and pragmatics of dialogue, p 8390
Murdock JW (Guest Editor) (2012) This is Watson. IBM J Res Dev 56(3/4)
Qu C, Ji F, Qiu M, Yang L (2018) Learning to selectively transfer: reinforced transfer learning for deep text matching. arXiv:1812.11561v1 [cs.IR] 30 Dec 2018
Prokhorenkova L et al (2018) CatBoost: unbiased boosting with categorical features. In: Advances in neural information processing systems
Dorogush AV, Ershov V, Gulin A (2018) CatBoost: gradient boosting with categorical features support. arXiv preprint arXiv:1810.11363
Micci-Barreca D (2001) A preprocessing scheme for high-cardinality categorical attributes in classification and prediction problems. ACM SIGKDD Explor Newsl 3(1):27–32
Cestnik B et al (1990) Estimating probabilities: a crucial task in machine learning. ECAI 90:147–149
Moholkar K, Patil S (2019) Hybrid CNN-LSTM model for answer identification. Int J Recent Technol Eng (IJRTE) 8(3). ISSN: 2277-3878
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Moholkar, K.P., Patil, S.H. (2021). Deep Ensemble Approach for Question Answer System. In: Pandian, A., Fernando, X., Islam, S.M.S. (eds) Computer Networks, Big Data and IoT. Lecture Notes on Data Engineering and Communications Technologies, vol 66. Springer, Singapore. https://doi.org/10.1007/978-981-16-0965-7_2
Download citation
DOI: https://doi.org/10.1007/978-981-16-0965-7_2
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-0964-0
Online ISBN: 978-981-16-0965-7
eBook Packages: EngineeringEngineering (R0)