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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 – 13 of 13 1 NOAHQA: Numerical Reasoning with Interpretable Graph Question Answering Dataset ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Jiang, Jing; Lim, Ee-Peng. - : Underline Science Inc., 2021 BASE Show details 2 NOAHQA: Numerical Reasoning with Interpretable Graph Question Answering Dataset ... Zhang, Qiyuan; Wang, Lei; Yu, Sicheng. - : arXiv, 2021 BASE Show details 3 Investigating Math Word Problems using Pretrained Multilingual Language Models ... Tan, Minghuan; Wang, Lei; Jiang, Lingxiao. - : arXiv, 2021 BASE Show details 4 Modeling Transitions of Focal Entities for Conversational Knowledge Base Question Answering ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Jiang, Jing; LAN, YUNSHI. - : Underline Science Inc., 2021 BASE Show details 5 Unsupervised Deep Structured Semantic Models for Commonsense Reasoning ... Wang, Shuohang; Zhang, Sheng; Shen, Yelong; Liu, Xiaodong; Liu, Jingjing; Gao, Jianfeng; Jiang, Jing. - : arXiv, 2019 Abstract: Commonsense reasoning is fundamental to natural language understanding. While traditional methods rely heavily on human-crafted features and knowledge bases, we explore learning commonsense knowledge from a large amount of raw text via unsupervised learning. We propose two neural network models based on the Deep Structured Semantic Models (DSSM) framework to tackle two classic commonsense reasoning tasks, Winograd Schema challenges (WSC) and Pronoun Disambiguation (PDP). Evaluation shows that the proposed models effectively capture contextual information in the sentence and co-reference information between pronouns and nouns, and achieve significant improvement over previous state-of-the-art approaches. ... : To appear in NAACL 2019, 10 pages ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://arxiv.org/abs/1904.01938 https://dx.doi.org/10.48550/arxiv.1904.01938 BASE Hide details 6 Cross-lingual semantic specialization via lexical relation induction Glavaš, Goran; Vulić, Ivan; Korhonen, Anna. - : Association for Computational Linguistics, 2019 BASE Show details 7 Do we really need fully unsupervised cross-lingual embeddings? Vulić, Ivan; Glavaš, Goran; Reichart, Roi. - : Association for Computational Linguistics, 2019 BASE Show details 8 Neural Synchronization during Face-to-Face Communication Jiang, Jing; Dai, Bohan; Peng, Danling. - : Society for Neuroscience, 2012 BASE Show details 9 UDEL/SMU at TREC 2009 Entity Track Zheng, Wei; Gottipati, Swapna; Jiang, Jing... In: DTIC (2009) BASE Show details 10 An empirical study of tokenization strategies for biomedical information retrieval Jiang, Jing; Zhai, ChengXiang In: Information Retrieval Journal. - Dordrecht [u.a.] : Springer Science + Business Media B.V. 10 (2007) 4-5, 341-363 BLLDB Show details 11 Diagnostic precision of tumor markers for malignant pleural effusion: a meta-analysis Liang, Qiu-Li; Shi, Huan-Zhong; Qin, Xue-Jun. - : BMJ Publishing Group Ltd, 2007 BASE Show details 12 Racial mimesis: Translation, literature, and self -fashioning in modern China. Jiang, Jing. - 2006 BASE Show details 13 An Empirical Study of Tokenization Strategies for Biomedical Information Retrieval Jiang, Jing; Zhai, ChengXiang. - 2006 BASE Show details

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