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1A Transformer Model for Manifesto Classification Using Cross-Context Training: An Ecuadorian Case Study
F. Barzallo, M. Baldeon-Calisto, M. Pérez, M. Moscoso, D. Navarrete, D. Riofr\'ıo, P. Medina-Peréz, S. Lai-Yuen, D. Ben\'ıtez, N. Peréz and 1 other author(s). Social Science Computer Review, (2024)
2 months ago by @pfcdgayo
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NLP
ML
classification
NLPMLclassification
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1Offline Regularised Reinforcement Learning for Large Language Models Alignment
P. Richemond, Y. Tang, D. Guo, D. Calandriello, M. Azar, R. Rafailov, B. Pires, E. Tarassov, L. Spangher, W. Ellsworth and 8 other author(s). (May 2024)
5 months ago by @tomvoelker
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nlp
dro
rl
nlpdrorl
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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)
10 months ago by @chiir_demo
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gpt-3
nlp
openai
gpt
llm
languagemodel
gpt-3nlpopenaigptllmlanguagemodel
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2SpanBERT: Improving Pre-training by Representing and Predicting Spans
M. Joshi, D. Chen, Y. Liu, D. Weld, L. Zettlemoyer, and O. Levy. (Jul 24, 2019)
10 months ago by @chiir_demo
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SpanBERT
NLP
posted_with_chatgpt
edited_with_chatgpt
sys:related_work:06be18119e083067ac69e8d9c02769f1
DeepLearning
PreTraining
LanguageModel
SpanBERTNLPposted_with_chatgptedited_with_chatgptsys:related_work:06be18119e083067ac69e8d9c02769f1DeepLearningPreTrainingLanguageModel
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2An analysis of the user occupational class through Twitter content.
D. Preotiuc-Pietro, V. Lampos, and N. Aletras. ACL (1), page 1754-1764. The Association for Computer Linguistics, (2015)
4 years ago by @janpf
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nlp
occupation
user2vec
embedding
motiv
nlpoccupationuser2vecembeddingmotiv
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1A Systematic Review of Natural Language Processing Applied to Radiology Reports
A. Casey, E. Davidson, M. Poon, H. Dong, D. Duma, A. Grivas, C. Grover, V. Suárez-Paniagua, R. Tobin, W. Whiteley and 2 other author(s). (2021)cite arxiv:2102.09553.
4 years ago by @hangdong
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clinical_notes
myown
systematic_review
edinburgh
usher
nlp
natural-language-processing
radiology
review
radiology_reports
survey
clinical_nlp
knowledge
computational_linguistics
clinical_notesmyownsystematic_reviewedinburghushernlpnatural-language-processingradiologyreviewradiology_reportssurveyclinical_nlpknowledgecomputational_linguistics
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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 @louissf
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nlp
ai
gpt
nlpaigpt
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7Cheap and Fast -- But is it Good? Evaluating Non-Expert Annotations for Natural Language Tasks
R. Snow, B. O'Connor, D. Jurafsky, and A. Ng. Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing, page 254--263. Honolulu, Hawaii, Association for Computational Linguistics, (October 2008)
4 years ago by @jaeschke
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nlp
annotation
expert
crowdsourcing
crowdworker
social
computing
natural
language
evaluation
processing
human
nlpannotationexpertcrowdsourcingcrowdworkersocialcomputingnaturallanguageevaluationprocessinghuman
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7Cheap and Fast - But is it Good? Evaluating Non-Expert Annotations for Natural Language Tasks.
R. Snow, B. O'Connor, D. Jurafsky, and A. Ng. EMNLP '08: Proceedings of the Conference on Empirical Methods in Natural Language Processing, page 254--263. Morristown, NJ, USA, Association for Computational Linguistics, (2008)
4 years ago by @ghagerer
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annotation-bias
nlp
emotion-recognition
inter-rater-agreement
crowdsourcing
emotion
dataset
affective-computing
annotation-biasnlpemotion-recognitioninter-rater-agreementcrowdsourcingemotiondatasetaffective-computing
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1QED: A Framework and Dataset for Explanations in Question Answering
M. Lamm, J. Palomaki, C. Alberti, D. Andor, E. Choi, L. Soares, and M. Collins. (2020)cite arxiv:2009.06354.
4 years ago by @bechr7
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nlp
dl
nlpdl
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2Text Reuse Detection using a Composition of Text Similarity Measures
D. Bär, T. Zesch, and I. Gurevych. Proceedings of COLING 2012, page 167--184. Mumbai, India, The COLING 2012 Organizing Committee, (December 2012)
4 years ago by @jaeschke
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nlp
detection
reuse
plagiarism
cls
text
nlpdetectionreuseplagiarismclstext
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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 @analyst
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nlp
deep-learning
2020
nlpdeep-learning2020
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19Learning Semantic Textual Similarity from Conversations
Y. Yang, S. Yuan, D. Cer, S. Kong, N. Constant, P. Pilar, H. Ge, Y. Sung, B. Strope, and R. Kurzweil. Proceedings of The Third Workshop on Representation Learning for NLP, page 164--174. Melbourne, Australia, Association for Computational Linguistics, (July 2018)
5 years ago by @blasp
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nlp
sts
mapping
relatedness
semantic
measures
ontology-matching
semantic-relatedness
knowledge
matching
ontology
nlpstsmappingrelatednesssemanticmeasuresontology-matchingsemantic-relatednessknowledgematchingontology
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19Learning Semantic Textual Similarity from Conversations
Y. Yang, S. Yuan, D. Cer, S. Kong, N. Constant, P. Pilar, H. Ge, Y. Sung, B. Strope, and R. Kurzweil. Proceedings of The Third Workshop on Representation Learning for NLP, page 164--174. Melbourne, Australia, Association for Computational Linguistics, (July 2018)
5 years ago by @theodoro
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nlp
sts
mapping
semantic
measures
semantic-relatedness
knowledge
matching
nlpstsmappingsemanticmeasuressemantic-relatednessknowledgematching
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19Learning Semantic Textual Similarity from Conversations
Y. Yang, S. Yuan, D. Cer, S. Kong, N. Constant, P. Pilar, H. Ge, Y. Sung, B. Strope, and R. Kurzweil. (2018)cite arxiv:1804.07754Comment: 10 pages, 8 Figures, 6 Tables.
5 years ago by @dawa
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nlp
measures
similarity
semantics
knowledge
alignment
matching
nlpmeasuressimilaritysemanticsknowledgealignmentmatching
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19Learning Semantic Textual Similarity from Conversations
Y. Yang, S. Yuan, D. Cer, S. Kong, N. Constant, P. Pilar, H. Ge, Y. Sung, B. Strope, and R. Kurzweil. Proceedings of The Third Workshop on Representation Learning for NLP, page 164--174. Melbourne, Australia, Association for Computational Linguistics, (July 2018)
5 years ago by @dawa
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nlp
sts
mapping
semantic
measures
semantic-relatedness
matching
nlpstsmappingsemanticmeasuressemantic-relatednessmatching
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11SemEval-2017 Task 1: Semantic Textual Similarity Multilingual and Crosslingual Focused Evaluation
D. Cer, M. Diab, E. Agirre, I. Lopez-Gazpio, and L. Specia. Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), page 1--14. Vancouver, Canada, Association for Computational Linguistics, (August 2017)
5 years ago by @dawa
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nlp
mapping
semantic
measures
matching
nlpmappingsemanticmeasuresmatching
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19Learning Semantic Textual Similarity from Conversations
Y. Yang, S. Yuan, D. Cer, S. Kong, N. Constant, P. Pilar, H. Ge, Y. Sung, B. Strope, and R. Kurzweil. (2018)cite arxiv:1804.07754Comment: 10 pages, 8 Figures, 6 Tables.
5 years ago by @braun4
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nlp
semantic
similarity
semantics
knowledge
alignment
matching
nlpsemanticsimilaritysemanticsknowledgealignmentmatching
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11SemEval-2017 Task 1: Semantic Textual Similarity Multilingual and Crosslingual Focused Evaluation.
D. Cer, M. Diab, E. Agirre, I. Lopez-Gazpio, and L. Specia. SemEval@ACL, page 1-14. The Association for Computer Linguistics, (2017)
5 years ago by @braun4
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nlp
sts
nlo
semantic
similarities
semantic-measures
similarity
knowledge
alignment
matching
nlpstsnlosemanticsimilaritiessemantic-measuressimilarityknowledgealignmentmatching
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11SemEval-2017 Task 1: Semantic Textual Similarity Multilingual and Crosslingual Focused Evaluation.
D. Cer, M. Diab, E. Agirre, I. Lopez-Gazpio, and L. Specia. SemEval@ACL, page 1-14. The Association for Computer Linguistics, (2017)
5 years ago by @dawa
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nlp
mapping
measures
merging
semantic-measures
benchmarking
similarity
semantic-measure
alignment
matching
nlpmappingmeasuresmergingsemantic-measuresbenchmarkingsimilaritysemantic-measurealignmentmatching
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