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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 – 16 of 16 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 Translate and label! An encoder-decoder approach for cross-lingual semantic role labeling Daza, Angel [Verfasser]; Frank, Anette [Verfasser]; Inui, Kentaro [Herausgeber]. - Mannheim : Leibniz-Institut für Deutsche Sprache (IDS), Bibliothek, 2019 DNB Subject Category Language Show details 6 Unsupervised Deep Structured Semantic Models for Commonsense Reasoning ... Wang, Shuohang; Zhang, Sheng; Shen, Yelong. - : arXiv, 2019 BASE Show details 7 Cross-lingual semantic specialization via lexical relation induction Glavaš, Goran; Vulić, Ivan; Korhonen, Anna. - : Association for Computational Linguistics, 2019 BASE Show details 8 Do we really need fully unsupervised cross-lingual embeddings? Vulić, Ivan; Glavaš, Goran; Reichart, Roi. - : Association for Computational Linguistics, 2019 BASE Show details 9 Neural control of rising and falling tones in Mandarin speakers who stutter Jiang, Jing; Howell, Peter; Lu, Chunming... In: Brain & language. - Orlando, Fla. [u.a.] : Elsevier 123 (2012) 3, 211-221 BLLDB OLC Linguistik Show details 10 Neural Synchronization during Face-to-Face Communication Jiang, Jing; Dai, Bohan; Peng, Danling. - : Society for Neuroscience, 2012 BASE Show details 11 Generating templates of entity summaries with an entity-aspect model and pattern mining Li, Peng; Jiang, Jing; Wang, Yinglin In: Association for Computational Linguistics. Proceedings of the conference. - Stroudsburg, Penn. : ACL 48 (2010) 1, 640-649 BLLDB Show details 12 UDEL/SMU at TREC 2009 Entity Track Zheng, Wei; Gottipati, Swapna; Jiang, Jing... In: DTIC (2009) BASE Show details 13 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 14 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 15 Racial mimesis: Translation, literature, and self -fashioning in modern China. Jiang, Jing. - 2006 BASE Show details 16 An Empirical Study of Tokenization Strategies for Biomedical Information Retrieval Jiang, Jing; Zhai, ChengXiang. - 2006 Abstract: Due to the great variation of biological names in biomedical text, appropriate tokenization is an important preprocessing step for biomedical IR. Despite its importance, there has been little study on the evaluation of various tokenization strategies for biomedical text. In this work, we conducted a careful, systematic evaluation of a set of tokenization heuristics on all the available TREC biomedical IR test collections with two representative retrieval methods. We also studied the effect of stemming and stop word removal on the retrieval performance. As expected, our experimental results show that tokenization can significantly affect the retrieval accuracy; appropriate tokenization can improve the performance by up to 80\%. In particular, it is shown that different query types require different tokenization heuristics, stemming is effective only for certain queries, and stop word removal in general does not improve the retrieval performance in biomedical text. Keyword: bioinformatics; information retrieval; tokenization URL: http://hdl.handle.net/2142/11207 BASE Hide details

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