Semantic Question Matching in Data Constrained Environment

Maitra, Anutosh; Sengupta, Shamik; Mukhopadhyay, Abhisek; Gupta, Deepak; Pujari, Rajkumar; Bhattacharya, Pushpak; Ekbal, Asif; Jain, Tom Geo

doi:10.1007/978-3-030-00794-2_29

Cited by 3 publications

(2 citation statements)

References 13 publications

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“…In recent times, there have been several studies on deep learning based reading comprehension/ QA (Hermann et al, 2015;Cui et al, 2017;Shen et al, 2017;Wang et al, 2017b;Gupta et al, 2018c;Wang and Jiang, 2016;Berant et al, 2014;Maitra et al, 2018;Cheng et al, 2016;Trischler et al, 2016). To the best of our knowledge, this is the very first attempt to automatically generate the code-mixed questions (i.e.…”

Section: Related Workmentioning

confidence: 98%

Uncovering Code-Mixed Challenges: A Framework for Linguistically Driven Question Generation and Neural Based Question Answering

Gupta

Lenka²,

Ekbal

et al. 2018

Proceedings of the 22nd Conference on Computational Natural Language Learning

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Existing research on question answering (QA) and comprehension reading (RC) are mainly focused on the resource-rich language like English. In recent times, there has been a rapid growth of multilingual contents on the web, and this has posed several challenges to the existing QA systems. Code-mixing is one such challenge that makes the task even more complex. In this paper, we propose a linguistically motivated technique for codemixed question generation (CMQG) and a neural network based architecture for code-mixed question answering (CMQA). For evaluation, we manually create the code-mixed questions for Hindi-English language pair. In order to show the effectiveness of our neural network based CMQA technique, we utilize two benchmark datasets, viz. SQuAD and MMQA. Experiments show that our proposed model achieves encouraging performance on CMQG and CMQA.

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Section: Related Workmentioning

confidence: 98%

Uncovering Code-Mixed Challenges: A Framework for Linguistically Driven Question Generation and Neural Based Question Answering

Gupta

Lenka²,

Ekbal

et al. 2018

Proceedings of the 22nd Conference on Computational Natural Language Learning

Self Cite

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“…Specifically, we use two different encoders, namely Convolutional Neural Network (CNN) and attention based Recurrent Neural Network (RNN). The effectiveness of CNN and RNN based encoder has been proven in other NLP tasks (Gupta et al, 2018d;Maitra et al, 2018;Gupta et al, 2018a,c). The CNN encoder uses multiple fully-connected over the convolution layer while the RNN encoder uses a LSTM layer with the attention (Raffel and Ellis, 2015) followed by multiple fully-connected layers.…”

Section: Proposed Deep Learning Modelmentioning

confidence: 99%

Helping each Other: A Framework for Customer-to-Customer Suggestion Mining using a Semi-supervised Deep Neural Network

Golchha,

Gupta,

Ekbal

et al. 2018

Preprint

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Suggestion mining is increasingly becoming an important task along with sentiment analysis. In today's cyberspace world, people not only express their sentiments and dispositions towards some entities or services, but they also spend considerable time sharing their experiences and advice to fellow customers and the product/service providers with two-fold agenda: helping fellow customers who are likely to share a similar experience, and motivating the producer to bring specific changes in their offerings which would be more appreciated by the customers. In our current work, we propose a hybrid deep learning model to identify whether a review text contains any suggestion. The model employs semi-supervised learning to leverage the useful information from the large amount of unlabeled data. We evaluate the performance of our proposed model on a benchmark customer review dataset, comprising of the reviews of Hotel and Electronics domains. Our proposed approach shows the F-scores of 65.6% and 65.5% for the Hotel and Electronics review datasets, respectively. These performances are significantly better compared to the existing state-of-the-art system.

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Exploring Disorder-Aware Attention for Clinical Event Extraction

Yadav

Ramteke

Ekbal

et al. 2020

ACM Trans. Multimedia Comput. Commun. Appl.

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Event extraction is one of the crucial tasks in biomedical text mining that aims to extract specific information concerning incidents embedded in the texts. In this article, we propose a deep learning framework that aims to identify the attributes (severity, course, temporal expression, and document creation time) associated with the medical concepts extracted from electronic medical records. The bi-directional long short-term memory network assisted by the attention mechanism is utilized to uncover the important aspects of the patient’s medical conditions. The attention mechanism specific to the medical disorder mention can focus on various parts of the sentence when different disorders are considered as input. The proposed methodology is evaluated on benchmark ShARe/CLEF eHealth Evaluation Lab 2014 shared task 2 datasets. In addition to the CLEF dataset, we also used the social media text, especially the medical blog posts. Experimental results of the proposed approach illustrate that our proposed approach achieves significant performance improvements over the state-of-the-art techniques and the highly competitive deep learning--based baseline methods.

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Semantic Question Matching in Data Constrained Environment

Cited by 3 publications

References 13 publications

Uncovering Code-Mixed Challenges: A Framework for Linguistically Driven Question Generation and Neural Based Question Answering

Uncovering Code-Mixed Challenges: A Framework for Linguistically Driven Question Generation and Neural Based Question Answering

Helping each Other: A Framework for Customer-to-Customer Suggestion Mining using a Semi-supervised Deep Neural Network

Exploring Disorder-Aware Attention for Clinical Event Extraction

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