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1
NOAHQA: Numerical Reasoning with Interpretable Graph Question Answering Dataset ...
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2
NOAHQA: Numerical Reasoning with Interpretable Graph Question Answering Dataset ...
Zhang, Qiyuan; Wang, Lei; Yu, Sicheng. - : arXiv, 2021
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3
Investigating Math Word Problems using Pretrained Multilingual Language Models ...
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4
Modeling Transitions of Focal Entities for Conversational Knowledge Base Question Answering ...
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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
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6
Unsupervised Deep Structured Semantic Models for Commonsense Reasoning ...
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7
Cross-lingual semantic specialization via lexical relation induction
Glavaš, Goran; Vulić, Ivan; Korhonen, Anna. - : Association for Computational Linguistics, 2019
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8
Do we really need fully unsupervised cross-lingual embeddings?
Vulić, Ivan; Glavaš, Goran; Reichart, Roi. - : Association for Computational Linguistics, 2019
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9
Neural control of rising and falling tones in Mandarin speakers who stutter
In: Brain & language. - Orlando, Fla. [u.a.] : Elsevier 123 (2012) 3, 211-221
BLLDB
OLC Linguistik
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10
Neural Synchronization during Face-to-Face Communication
Jiang, Jing; Dai, Bohan; Peng, Danling. - : Society for Neuroscience, 2012
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11
Generating templates of entity summaries with an entity-aspect model and pattern mining
In: Association for Computational Linguistics. Proceedings of the conference. - Stroudsburg, Penn. : ACL 48 (2010) 1, 640-649
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12
UDEL/SMU at TREC 2009 Entity Track
In: DTIC (2009)
Abstract: We report our methods and experiment results from the collaborative participation of the InfoLab group from University of Delaware and the school of Information Systems from Singapore Management University in the TREC 2009 Entity track. Our general goal is to study how we may apply language modeling approaches and natural language processing techniques to the task. Specifically, we proposed to find supporting information based on segment retrieval, to extract entities using Stanford NER tagger, and to rank entities based on a previously proposed probabilistic framework for expert finding. ; Presented at the Text Retrieval Conference (18th) (TREC-2009) held in Gaithersburg, MD, on 17-20 Nov 2009. Prepared in cooperation with Singapore Management University. Sponsored in part by the Defense Advanced Research Projects Agency (DARPA), and the Advanced Research and Development Activity (ARDA).
Keyword: *INFORMATION RETRIEVAL; *MODELS; *NATURAL LANGUAGE; *TREC(TEXT RETRIEVAL EXPERIMENTATION); COLLABORATIVE TECHNIQUES; DATA PROCESSING; EXPERIMENTAL DATA; EXPERT SYSTEMS; INFORMATION PROCESSING; Information Science; METHODOLOGY; NER(NAMED ENTITY RECOGNITION); PROBABILITY; RANKING; RECOGNITION; SMU(SINGAPORE MANAGEMENT UNIVERSITY); SYMPOSIA; UDEL(UNIVERSITY OF DELAWARE)
URL: http://oai.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADA517766
http://www.dtic.mil/docs/citations/ADA517766
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13
An empirical study of tokenization strategies for biomedical information retrieval
In: Information Retrieval Journal. - Dordrecht [u.a.] : Springer Science + Business Media B.V. 10 (2007) 4-5, 341-363
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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
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15
Racial mimesis: Translation, literature, and self -fashioning in modern China.
Jiang, Jing. - 2006
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16
An Empirical Study of Tokenization Strategies for Biomedical Information Retrieval
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