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Bootstrapping via Graph Propagation
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In: http://www.cs.sfu.ca/~anoop/students/mwhitney/mwhitney-mscThesis.pdf
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| 162 |
He Y: Methodological Review: Extracting interactions between proteins from the literature
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In: http://people.kmi.open.ac.uk/yulan/PAPERS/JBI08.pdf
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| 163 |
LING 5320 INTRODUTION TO CORPUS LINGUISTICS GENERAL INFO
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In: http://www.lingtechcomm.unt.edu/~swulff/teaching/5320_syllabus.pdf
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| 164 |
Experience
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In: http://www.desilinguist.org/pdf/madnani-cv.pdf
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| 165 |
1 SCIENCE AND TECHNOLOGY INNOVATION by
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In: http://www.au.af.mil/au/awc/awcgate/navy/onr_scitech_innov.pdf
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| 166 |
Semantic Relatedness Study Using Second Order Co-occurrence Vectors Computed from
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In: http://www.d.umn.edu/~tpederse/Pubs/ihi2012-liu.pdf
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chapter 12 Computationally discriminating literary from non-literary texts
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In: http://www.madresearchlab.org/Selected_Publications_files/LouwerseBeneshZhang2008.pdf
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| 168 |
Contextual Vocabulary Acquisition: From Algorithm to Curriculum
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In: http://www.cse.buffalo.edu/~rapaport/CVA/reading4CgSJnl.pdf
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| 169 |
Taxonomy and Evaluation of Markers for Computational
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In: http://world-comp.org/p2011/ICA3763.pdf
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Abstract:
Abstract- Currently, stylistic analysis of natural language texts is achieved through a wide variety of techniques containing many different algorithms, feature sets and collection methods. Most machine-learning methods rely on feature extraction to model the text and perform classification. But what are the best features for making style based distinctions? While many researchers have developed particular collections of style features – called style markers – no definitive list exists. In this paper we present an organized collection of such style markers with performance data on a diverse set of texts. We show that for each training document, one or more markers exist that can distinguish it from others, providing a basis for a weighted, combined set of markers that outperform any of the individual ones. We examine and categorize 502 style markers, both individually and as a set, and evaluate their performance on several English language text collections.
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Keyword:
Artificial Intelligence; Computational Linguistics; Computational Stylistics; Machine Learning; Natural Language Processing; Style Processing
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URL: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.217.9569
http://world-comp.org/p2011/ICA3763.pdf
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| 170 |
Gradient for Structured Prediction
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In: http://www.cs.sfu.ca/%7Eanoop/students/porus_patell/poruspatell-mscProject.pdf
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| 171 |
Simulating and reconstructing language change
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In: http://www2.informatik.hu-berlin.de/sam/preprint/hochmuth220.pdf
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| 172 |
General
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In: http://hal.inria.fr/docs/00/10/31/09/PDF/haskell01-kow.pdf
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| 173 |
Sciences Islamabad
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In: http://www.ijcaonline.org/volume25/number3/pxc3874058.pdf
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| 174 |
Pacific Association for Computational Linguistics UNIFIED REPRESENTATION OF TYPOLOGICAL, ABSOLUTE AND RELATIONAL PREDICATES
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In: http://guillaume.pitel.free.fr/Files/UNIFIED%20REPRESENTATION%20OF%20PREDICATES%20-%20PACLING%20-%20PITEL.pdf
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| 175 |
SCIENCE AND TECHNOLOGY TEXT MINING: WIRELESS LANS By
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In: http://www.ice.nsysu.edu.tw/English_version/news/050927_wireless_technical_report.pdf
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| 176 |
Handsome Non-Commutative Proof-Nets: perfect matchings, series-parallel orders and Hamiltonian circuits
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In: http://hal.inria.fr/docs/00/07/12/48/PDF/RR-5409.pdf
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| 177 |
Formal structure of Sanskrit text: Requirements analysis for a mechanical Sanskrit processor
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In: http://yquem.inria.fr/~huet/PUBLIC/Brown.pdf
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| 178 |
Hamming-like distances for ill-defined strings in linguistic
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In: http://www.dmi.units.it/~bortolu/files/journals/bortolussisgarro-RIMUT07.pdf
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| 179 |
MACHINE LEARNING TECHNIQUES IN DIALOGUE ACT RECOGNITION
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In: http://www.cs.ut.ee/~fishel/doc/publ/darec.ery.pdf
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| 180 |
Roles and Relationships Roles‘07: The 2 nd Workshop on Roles and Relationships in Object Oriented Programming, Multiagent Systems
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In: http://www.di.unito.it/~guido/violator/ecoopreader/Roles07.pdf
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