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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 10 of 10 1 MetaXL: Meta Representation Transformation for Low-resource Cross-lingual Learning ... Xia, Mengzhou; Zheng, Guoqing; Mukherjee, Subhabrata. - : arXiv, 2021 BASE Show details 2 XtremeDistilTransformers: Task Transfer for Task-agnostic Distillation ... Mukherjee, Subhabrata; Awadallah, Ahmed Hassan; Gao, Jianfeng. - : arXiv, 2021 BASE Show details 3 Gender Bias in Multilingual Embeddings and Cross-Lingual Transfer ... Zhao, Jieyu; Mukherjee, Subhabrata; Hosseini, Saghar. - : arXiv, 2020 BASE Show details 4 XtremeDistil: Multi-stage Distillation for Massive Multilingual Models ... Mukherjee, Subhabrata; Awadallah, Ahmed. - : arXiv, 2020 BASE Show details 5 Smart To-Do : Automatic Generation of To-Do Items from Emails ... Mukherjee, Sudipto; Mukherjee, Subhabrata; Hasegawa, Marcello. - : arXiv, 2020 BASE Show details 6 Distilling BERT into Simple Neural Networks with Unlabeled Transfer Data ... Mukherjee, Subhabrata; Awadallah, Ahmed Hassan. - : arXiv, 2019 BASE Show details 7 DeClarE: Debunking Fake News and False Claims using Evidence-Aware Deep Learning ... Popat, Kashyap; Mukherjee, Subhabrata; Yates, Andrew. - : arXiv, 2018 BASE Show details 8 People on Drugs: Credibility of User Statements in Health Communities ... Mukherjee, Subhabrata; Weikum, Gerhard; Danescu-Niculescu-Mizil, Cristian. - : arXiv, 2017 Abstract: Online health communities are a valuable source of information for patients and physicians. However, such user-generated resources are often plagued by inaccuracies and misinformation. In this work we propose a method for automatically establishing the credibility of user-generated medical statements and the trustworthiness of their authors by exploiting linguistic cues and distant supervision from expert sources. To this end we introduce a probabilistic graphical model that jointly learns user trustworthiness, statement credibility, and language objectivity. We apply this methodology to the task of extracting rare or unknown side-effects of medical drugs --- this being one of the problems where large scale non-expert data has the potential to complement expert medical knowledge. We show that our method can reliably extract side-effects and filter out false statements, while identifying trustworthy users that are likely to contribute valuable medical information. ... Keyword: Artificial Intelligence cs.AI; Computation and Language cs.CL; FOS Computer and information sciences; Information Retrieval cs.IR; Machine Learning stat.ML; Social and Information Networks cs.SI URL: https://arxiv.org/abs/1705.02522 https://dx.doi.org/10.48550/arxiv.1705.02522 BASE Hide details 9 Probabilistic graphical models for credibility analysis in evolving online communities Mukherjee, Subhabrata. - 2017 BASE Show details 10 WikiSent : Weakly Supervised Sentiment Analysis Through Extractive Summarization With Wikipedia ... Mukherjee, Subhabrata; Bhattacharyya, Pushpak. - : arXiv, 2012 BASE Show details

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