Saichethan Reddy
2020
Detecting Tweets Reporting Birth Defect Pregnancy Outcome Using Two-View CNN RNN Based Architecture
Saichethan Reddy
Proceedings of the Fifth Social Media Mining for Health Applications Workshop & Shared Task
This research work addresses a new multi-class classification task (fifth task) provided at the fifth Social Media Mining for Health Applications (SMM4H) workshop. This automatic tweet classification task involves distinguishing three classes of tweets that mention birth defects. We propose a novel two view based CNN-BiGRU based architectures for this task. Experimental evaluation of our proposed architecture over the validation set gives encouraging results as it improves by approximately 7% over our single view model for the fifth task. Code of our proposed framework is made available on Github
IIITBH-IITP@CL-SciSumm20, CL-LaySumm20, LongSumm20
Saichethan Reddy
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Naveen Saini
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Sriparna Saha
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Pushpak Bhattacharyya
Proceedings of the First Workshop on Scholarly Document Processing
In this paper, we present the IIIT Bhagalpur and IIT Patna team’s effort to solve the three shared tasks namely, CL-SciSumm 2020, CL-LaySumm 2020, LongSumm 2020 at SDP 2020. The theme of these tasks is to generate medium-scale, lay and long summaries, respectively, for scientific articles. For the first two tasks, unsupervised systems are developed, while for the third one, we develop a supervised system.The performances of all the systems were evaluated on the associated datasets with the shared tasks in term of well-known ROUGE metric.
IIITBH at WNUT-2020 Task 2: Exploiting the best of both worlds
Saichethan Reddy
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Pradeep Biswal
Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)
In this paper, we present IIITBH team’s effort to solve the second shared task of the 6th Workshop on Noisy User-generated Text (W-NUT)i.e Identification of informative COVID-19 English Tweets. The central theme of the task is to develop a system that automatically identify whether an English Tweet related to the novel coronavirus (COVID-19) is Informative or not. Our approach is based on exploiting semantic information from both max pooling and average pooling, to this end we propose two models.
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