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2Probabilistic Syntax
C. Manning. Probabilistic Linguistics, chapter 8, The MIT Press, (April 2003)
4 months ago by @jaeschke
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nlp
linguistics
language
corpus
probability
syntax
nlplinguisticslanguagecorpusprobabilitysyntax
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1The importance of matching language to type of evidence: avoiding the pitfalls of reporting outcomes data.
P. Kohli, and C. Cannon. Clinical cardiology, 35 (12): 714-7 (December 2012)CI: (c) 2012; JID: 7903272; 2012/09/04 received; 2012/09/10 accepted; 2012/11/28 aheadofprint; ppublish;.
2 years ago by @jepcastel
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BiomedicalResearch
Language
BiomedicalResearchLanguage
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2GreaseLM: Graph REASoning Enhanced Language Models
X. Zhang, A. Bosselut, M. Yasunaga, H. Ren, P. Liang, C. Manning, and J. Leskovec. International Conference on Learning Representations, (2021)
3 years ago by @becker
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nlp
toread
reasoning
neural
language
model
knowledge
network
jure
graph
nlptoreadreasoningneurallanguagemodelknowledgenetworkjuregraph
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3Pre-Training Transformers as Energy-Based Cloze Models.
K. Clark, M. Luong, Q. Le, and C. Manning. EMNLP (1), page 285-294. Association for Computational Linguistics, (2020)
4 years ago by @nosebrain
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cloze
language
pertaining
transformer
model
clozelanguagepertainingtransformermodel
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10Language Models are Few-Shot Learners
T. Brown, B. Mann, N. Ryder, M. Subbiah, J. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell and 21 other author(s). (2020)cite arxiv:2005.14165Comment: 40+32 pages.
4 years ago by @parismic
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language
few
learning
gpt
model
shot
languagefewlearninggptmodelshot
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7ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators
K. Clark, M. Luong, Q. Le, and C. Manning. International Conference on Learning Representations, (2020)
4 years ago by @nosebrain
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electra
language
generator
discriminator
text
encoder
model
electralanguagegeneratordiscriminatortextencodermodel
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3Do Massively Pretrained Language Models Make Better Storytellers?
A. See, A. Pappu, R. Saxena, A. Yerukola, and C. Manning. (2019)cite arxiv:1909.10705Comment: Accepted to CoNLL 2019.
5 years ago by @albinzehe
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kallimachos
language
model
neuralnets
kallimachoslanguagemodelneuralnets
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15Deep contextualized word representations
M. Peters, M. Neumann, M. Iyyer, M. Gardner, C. Clark, K. Lee, and L. Zettlemoyer. (2018)cite arxiv:1802.05365Comment: NAACL 2018. Originally posted to openreview 27 Oct 2017. v2 updated for NAACL camera ready.
5 years ago by @nosebrain
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models
language
elmo
project:dzptm
modelslanguageelmoproject:dzptm
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3Do Massively Pretrained Language Models Make Better Storytellers?
A. See, A. Pappu, R. Saxena, A. Yerukola, and C. Manning. (2019)cite arxiv:1909.10705Comment: Accepted to CoNLL 2019.
5 years ago by @hotho
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toread
kallimachos
language
model
toreadkallimachoslanguagemodel
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1Training Classifiers with Natural Language Explanations
B. Hancock, P. Varma, S. Wang, M. Bringmann, P. Liang, and C. Ré. arXiv preprint arXiv:1805.03818, (2018)
6 years ago by @thoni
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citedby:scholar:count:1
natural
language
citedby:scholar:timestamp:2018-11-5
classifier
training
citedby:scholar:count:1naturallanguagecitedby:scholar:timestamp:2018-11-5classifiertraining
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4Adversarial Generation of Natural Language
S. Subramanian, S. Rajeswar, F. Dutil, C. Pal, and A. Courville. ACL 2017, (2017)
7 years ago by @daschloer
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GAN
language
GANlanguage
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4A large annotated corpus for learning natural language inference
S. Bowman, G. Angeli, C. Potts, and C. Manning. Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP), Association for Computational Linguistics, (2015)
8 years ago by @thoni
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dataset
snli
inference
natural
language
corpus
datasetsnliinferencenaturallanguagecorpus
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13Foundations of Statistical Natural Language Processing
C. Manning, and H. Schütze. MIT Press, (June 1999)
10 years ago by @jennymac
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statistical
language
statisticallanguage
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1Human language reveals a universal positivity bias
P. Dodds, E. Clark, S. Desu, M. Frank, A. Reagan, J. Williams, L. Mitchell, K. Harris, I. Kloumann, J. Bagrow and 4 other author(s). Proceedings of the National Academy of Sciences, (2015)
10 years ago by @asmelash
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language
language
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1Cognitive impairment after unilateral hemispheric injury of congenital or adult origin.
S. Rogers, C. Coe, and K. Hartke. Am J Occup Ther, 56 (2): 191--201 (2002)
10 years ago by @ar0berts
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Disorders;
Humans;
Laterality;
Space
Adult;
Variance;
Cerebral
Male;
Cognition
Therapy;
Occupational
Accident;
Functional
Analysis
Perception;
Palsy;
Female;
of
Perception
Speech
Language
Cerebrovascular
Disorders;Humans;Laterality;SpaceAdult;Variance;CerebralMale;CognitionTherapy;OccupationalAccident;FunctionalAnalysisPerception;Palsy;Female;ofPerceptionSpeechLanguageCerebrovascular
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2Eugene – A Domain Specific Language for Specifying and Constraining Synthetic Biological Parts, Devices, and Systems
L. Bilitchenko, A. Liu, S. Cheung, E. Weeding, B. Xia, M. Leguia, J. Anderson, and D. Densmore. PLoS ONE, 6 (4): e18882 (April 2011)
11 years ago by @mhwombat
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domain
language
dsl
specific
domainlanguagedslspecific
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2Foundations of statistical natural language processing
C. Manning, and H. Schütze. MIT press, Cambridge (Mass.), (2001)
11 years ago by @lovebooks
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NLP
Text_Mining
statistical
natural
language
foundations
processing
NLPText_Miningstatisticalnaturallanguagefoundationsprocessing
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3No country for old members: user lifecycle and linguistic change in online communities
C. Danescu-Niculescu-Mizil, R. West, D. Jurafsky, J. Leskovec, and C. Potts. Proceedings of the 22nd international conference on World Wide Web, page 307--318. Republic and Canton of Geneva, Switzerland, International World Wide Web Conferences Steering Committee, (2013)
11 years ago by @jil
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semc
lifecycle
paper
language
www
2013
adoption
user
best
community
semclifecyclepaperlanguagewww2013adoptionuserbestcommunity
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38Foundations of statistical natural language processing
C. Manning, and H. Schütze. MIT Press, Cambridge, Mass., (1999)
12 years ago by @lopusz_kdd
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language
language
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6Building Watson: An Overview of the DeepQA Project.
D. Ferrucci, E. Brown, J. Chu-Carroll, J. Fan, D. Gondek, A. Kalyanpur, A. Lally, J. Murdock, E. Nyberg, J. Prager and 2 other author(s). AI Magazine, 31 (3): 59-79 (2010)
13 years ago by @butonic
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nlp
answering
ibm
natural
language
question
ai
processing
watson
dis
qa
nlpansweringibmnaturallanguagequestionaiprocessingwatsondisqa
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2Probabilistic Syntax

1The importance of matching language to type of evidence: avoiding the pitfalls of reporting outcomes data.

2GreaseLM: Graph REASoning Enhanced Language Models

3Pre-Training Transformers as Energy-Based Cloze Models.

10Language Models are Few-Shot Learners

7ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators

3Do Massively Pretrained Language Models Make Better Storytellers?

15Deep contextualized word representations

3Do Massively Pretrained Language Models Make Better Storytellers?

1Training Classifiers with Natural Language Explanations

4Adversarial Generation of Natural Language

4A large annotated corpus for learning natural language inference

13Foundations of Statistical Natural Language Processing

1Human language reveals a universal positivity bias

1Cognitive impairment after unilateral hemispheric injury of congenital or adult origin.

2Eugene – A Domain Specific Language for Specifying and Constraining Synthetic Biological Parts, Devices, and Systems

2Foundations of statistical natural language processing

3No country for old members: user lifecycle and linguistic change in online communities

38Foundations of statistical natural language processing

6Building Watson: An Overview of the DeepQA Project.

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