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Additional file 1: of A preliminary validation of the Brief COPE instrument for assessing coping strategies among people living with HIV in China ...
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Additional file 1: of A preliminary validation of the Brief COPE instrument for assessing coping strategies among people living with HIV in China ...
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Toward Enhancing Automated Credibility Assessment: A Model for Question Type Classification and Tools for Linguistic Analysis. Unpublished Dissertation
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In: http://arizona.openrepository.com/arizona/bitstream/10150/145456/1/azu_etd_11559_sip1_m.pdf (2011)
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Intra-sentence punctuation insertion in natural language generation
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In: ftp://ftp.research.microsoft.com/pub/tr/tr-2002-58.pdf (2002)
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Experiments in Single and Multi-Document Summarization Using MEAD
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In: http://perun.si.umich.edu/~radev/papers/duc01.ps (2001)
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CST Bank: A Corpus for the Study of Cross-document Structural Relationships
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In: http://tangra.si.umich.edu/~radev/papers/lrec-cst04.pdf
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CST Bank: A Corpus for the Study of Cross-document Structural Relationships
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In: http://www.lrec-conf.org/proceedings/lrec2004/pdf/411.pdf
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Mining Inter-Entity Semantic Relations Using Improved Transductive Learning
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In: http://tangra.si.umich.edu/~radev/papers/c003.pdf
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Abstract:
Abstract. This paper studies the problem of mining relational data hidden in natural language text. In particular, it approaches the relation classification problem with the strategy of transductive learning. Different algorithms are presented and empirically evaluated on the ACE corpus. We show that transductive learners exploiting various lexical and syntactic features can achieve promising classification performance. More importantly, transductive learning performance can be significantly improved by using an induced similarity function. 1
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URL: http://tangra.si.umich.edu/~radev/papers/c003.pdf http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.139.3311
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Tense Tagging for Verbs in Cross-Lingual Context: A Case Study
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In: http://tangra.si.umich.edu/~radev/papers/c004.pdf
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Question‐driven segmentation of lecture speech text: Towards intelligent e‐learning systems
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