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41
Hilbert’s epsilon as an operator of indefinite committed choice
In: http://www.ags.uni-sb.de/~cp/p/epsi/pdf.pdf (2006)
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42
Symbolic models of human sentence processing
In: http://www.ling.uni-potsdam.de/~vasishth/Papers/vasishthlewisElsevier.pdf (2005)
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43
Automated Video Segmentation for Lecture Videos: A . . .
In: http://www.business.hku.hk/~mchau/papers/AutomatedVideoSegmentation.pdf (2005)
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44
Finite State Morphology: The Turkish Nominal Paradigm
In: http://www.sfs.uni-tuebingen.de/iscl/Theses/makedonski.pdf (2005)
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45
Deriving marketing intelligence from online discussion
In: http://www.kamalnigam.com/papers/aw-kdd05.pdf (2005)
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46
Linguistics from a Computational Perspective─ Review of Computational Linguistics and Beyond
In: http://www.ee.cuhk.edu.hk/~lel/publications/papers/2005/2005_Gong_Language_%26_Linguistics.pdf (2005)
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47
Describing lambda terms in Context Unification
In: http://hal.inria.fr/docs/00/53/65/24/PDF/clls-cu.pdf (2005)
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48
Authors
In: http://repository.cmu.edu/cgi/viewcontent.cgi?article%3D1707%26context%3Dpsychology (2004)
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49
B.: Tackling the internet glossary glut: Automatic extraction and evaluation of genus phrases
In: http://www.cs.columbia.edu/nlp/papers/2003/klavans_al_03.pdf (2003)
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50
Verbal Constraint Management for Shape Conceptualization
In: http://dutoce.io.tudelft.nl/~jouke/docdb/docs/tmce2002verlinden.pdf (2002)
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51
Statistical Models for the Induction and Use of Selectional Preferences
In: http://www.mitre.org/work/tech_papers/tech_papers_02/greiff_statistical/greiff_statistical.pdf (2002)
Abstract: Selectional preferences have a long history in both generative and computational linguistics. However, 11 since the publication of Resnik’s dissertation in 1993, a new approach has surfaced in the computational 12 linguistics community. This new line of research combines knowledge represented in a pre-defined 13 semantic class hierarchy with statistical tools including information theory, statistical modeling, and 14 Bayesian inference. These tools are used to learn selectional preferences from examples in a corpus. 15 Instead of simple sets of semantic classes, selectional preferences are viewed as probability distributions 16 over various entities. We survey research that extends Resnik’s initial work, discuss the strengths and 17 weaknesses of each approach, and show how they together form a cohesive line of research.
Keyword: Computational linguistics; Learning; Selectional preferences; Statistical modeling
URL: http://www.mitre.org/work/tech_papers/tech_papers_02/greiff_statistical/greiff_statistical.pdf
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.109.1188
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52
Inferring Knowledge from a large semantic network. http://arxiv.org/ftp/cs/papers/0501/0501072.pdf Accessed 27
In: https://hal.archives-ouvertes.fr/hal-00004061/document/ (2002)
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53
Statistical Models for the Induction and Use of Selectional Preferences
In: http://que.info-science.uiowa.edu/~light/research/mypapers/lightCogSciFFF.pdf (2002)
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54
Protein names and how to find them
In: http://www.sics.se/~franzen/Artiklar/Ijmi/ijmi02franzen01.pdf (2002)
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55
Finding Syntactic Structure in Unparsed Corpora : The Gsearch corpus query system, Computer and Humanities
In: http://www.coli.uni-saarland.de/%7Ecrocker/pubs/chum00.pdf (2001)
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56
The Gsearch Corpus: Finding Syntactic Structure in Unparsed Corpora
In: http://www.hcrc.ed.ac.uk/gsearch/papers/chum_paper.pdf (2001)
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57
Three Perspectives on MT in the Classroom
In: http://www.eamt.org/summitVIII/./papers/somers-2.pdf (2001)
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58
Three Perspectives on MT in the Classroom
In: http://www.dlsi.ua.es/tmt/docum/TMT4.pdf (2001)
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59
Answer Extraction using a Dependency Grammar in ExtrAns
In: http://www.ics.mq.edu.au/~diego/publications/DGZurich.pdf (2000)
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60
C.: Current trends in grammatical inference
In: http://pagesperso.lina.univ-nantes.fr/%7Ecdlh/papers/colinsspr00.pdf (2000)
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