-
4
-
-
84904136037
-
Large-scale machine learning with stochastic gradient descent
-
Springer
-
Léon Bottou. 2010. Large-scale machine learning with stochastic gradient descent. In Proceedings of COMPSTAT'2010, Springer, pages 177-186.
-
(2010)
Proceedings of COMPSTAT'2010
, pp. 177-186
-
-
Bottou, L.1
-
6
-
-
0345007542
-
Named entity recognition: A maximum entropy approach using global information
-
Association for Computational Linguistics
-
Hai Leong Chieu and Hwee Tou Ng. 2002. Named entity recognition: a maximum entropy approach using global information. In Proceedings of the 19th international conference on Computational linguistics-Volume 1. Association for Computational Linguistics, pages 1-7. http://aclweb.org/anthology/C02-1025.
-
(2002)
Proceedings of The 19th International Conference on Computational Linguistics-Volume 1
, pp. 1-7
-
-
Chieu, H.L.1
Ng, H.T.2
-
7
-
-
80053558787
-
Natural language processing (almost) from scratch
-
Aug
-
Ronan Collobert, Jason Weston, Léon Bottou, Michael Karlen, Koray Kavukcuoglu, and Pavel Kuksa. 2011. Natural language processing (almost) from scratch. Journal of Machine Learning Research 12(Aug):2493-2537.
-
(2011)
Journal of Machine Learning Research
, vol.12
, pp. 2493-2537
-
-
Collobert, R.1
Weston, J.2
Bottou, L.3
Karlen, M.4
Kavukcuoglu, K.5
Kuksa, P.6
-
8
-
-
0003102944
-
Maximum likelihood estimation of observer error-rates using the em algorithm
-
Alexander Philip Dawid and Allan M Skene. 1979. Maximum likelihood estimation of observer error-rates using the em algorithm. Applied statistics pages 20-28.
-
(1979)
Applied Statistics
, pp. 20-28
-
-
Dawid, A.P.1
Skene, A.M.2
-
12
-
-
85094865799
-
Annotating named entities in twitter data with crowdsourcing
-
Association for Computational Linguistics
-
Tim Finin, Will Murnane, Anand Karandikar, Nicholas Keller, Justin Martineau, and Mark Dredze. 2010. Annotating named entities in twitter data with crowdsourcing. In Proceedings of the NAACL HLT 2010 Workshop on Creating Speech and Language Data with Amazon's Mechanical Turk. Association for Computational Linguistics, pages 80-88.
-
(2010)
Proceedings of The NAACL HLT 2010 Workshop on Creating Speech and Language Data with Amazon'S Mechanical Turk
, pp. 80-88
-
-
Finin, T.1
Murnane, W.2
Karandikar, A.3
Keller, N.4
Martineau, J.5
Dredze, M.6
-
18
-
-
84959922810
-
Estimation of discourse segmentation labels from crowd data
-
Association for Computational Linguistics, Lisbon, Portugal
-
Ziheng Huang, Jialu Zhong, and Rebecca J. Passon-neau. 2015. Estimation of discourse segmentation labels from crowd data. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, Lisbon, Portugal, pages 2190-2200. http://aclweb.org/anthology/D15-1261.
-
(2015)
Proceedings of The 2015 Conference on Empirical Methods in Natural Language Processing
, pp. 2190-2200
-
-
Huang, Z.1
Zhong, J.2
Passonneau, R.J.3
-
19
-
-
80053381171
-
Why doesn't em find good hmm pos-taggers?
-
Mark Johnson. 2007. Why doesn't em find good hmm pos-taggers? In EMNLP-CoNLL. pages 296-305. http://aclweb.org/anthology/D07-1031.
-
(2007)
EMNLP-CoNLL
, pp. 296-305
-
-
Johnson, M.1
-
22
-
-
84961376850
-
Convolutional neural networks for sentence classification
-
Association for Computational Linguistics, Doha, Qatar
-
Yoon Kim. 2014. Convolutional neural networks for sentence classification. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). Association for Computational Linguistics, Doha, Qatar, pages 1746-1751. http://www.aclweb.org/anthology/D14-1181.
-
(2014)
Proceedings of The 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)
, pp. 1746-1751
-
-
Kim, Y.1
-
23
-
-
0142192295
-
Conditional random fields: Probabilistic models for segmenting and labeling sequence data
-
John Lafferty, Andrew McCallum, and Fernando Pereira. 2001. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In Proceedings of the eighteenth international conference on machine learning, ICML. volume 1, pages 282-289.
-
(2001)
Proceedings of The Eighteenth International Conference on Machine Learning, ICML
, vol.1
, pp. 282-289
-
-
Lafferty, J.1
McCallum, A.2
Pereira, F.3
-
24
-
-
84994130883
-
Neural architectures for named entity recognition
-
Association for Computational Linguistics
-
Guillaume Lample, Miguel Ballesteros, Sandeep Sub-ramanian, Kazuya Kawakami, and Chris Dyer. 2016. Neural architectures for named entity recognition. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Association for Computational Linguistics, pages 260-270. https://doi.org/10.18653/v1/N16-1030.
-
(2016)
Proceedings of The 2016 Conference of The North American Chapter of The Association for Computational Linguistics: Human Language Technologies
, pp. 260-270
-
-
Lample, G.1
Ballesteros, M.2
Subramanian, S.3
Kawakami, K.4
Dyer, C.5
-
30
-
-
0022594196
-
An introduction to hidden markov models
-
Lawrence Rabiner and B Juang. 1986. An introduction to hidden markov models. ieee assp magazine 3(1):4-16.
-
(1986)
Ieee Assp Magazine
, vol.3
, Issue.1
, pp. 4-16
-
-
Rabiner, L.1
Juang, B.2
-
31
-
-
77951954464
-
Learning from crowds
-
Apr
-
Vikas C Raykar, Shipeng Yu, Linda H Zhao, Gerardo Hermosillo Valadez, Charles Florin, Luca Bogoni, and Linda Moy. 2010. Learning from crowds. Journal of Machine Learning Research 11(Apr):1297-1322.
-
(2010)
Journal of Machine Learning Research
, vol.11
, pp. 1297-1322
-
-
Raykar, V.C.1
Yu, S.2
Zhao, L.H.3
Valadez, G.H.4
Florin, C.5
Bogoni, L.6
Moy, L.7
-
32
-
-
84898827632
-
Sequence labeling with multiple annotators
-
Filipe Rodrigues, Francisco Pereira, and Bernardete Ribeiro. 2014. Sequence labeling with multiple annotators. Machine learning 95(2):165-181.
-
(2014)
Machine Learning
, vol.95
, Issue.2
, pp. 165-181
-
-
Rodrigues, F.1
Pereira, F.2
Ribeiro, B.3
-
34
-
-
34347381922
-
Utilization of the PICO framework to improve searching PubMed for clinical questions
-
Connie Schardt, Martha B Adams, Thomas Owens, Sheri Keitz, and Paul Fontelo. 2007. Utilization of the PICO framework to improve searching PubMed for clinical questions. BMC medical informatics and decision making 7(1):16.
-
(2007)
BMC Medical Informatics and Decision Making
, vol.7
, Issue.1
, pp. 16
-
-
Schardt, C.1
Adams, M.B.2
Owens, T.3
Keitz, S.4
Fontelo, P.5
-
36
-
-
80053360508
-
Cheap and fast - But is it good? Evaluating non-expert annotations for natural language tasks
-
Association for Computational Linguistics
-
Rion Snow, Brendan O'Connor, Daniel Jurafsky, and Andrew Ng. 2008. Cheap and fast - but is it good? evaluating non-expert annotations for natural language tasks. In Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, pages 254-263. http://aclweb.org/anthology/D081027.
-
(2008)
Proceedings of The 2008 Conference on Empirical Methods in Natural Language Processing
, pp. 254-263
-
-
Snow, R.1
O'Connor, B.2
Jurafsky, D.3
Ng, A.4
-
38
-
-
84909581998
-
Community-based Bayesian aggregation models for crowdsourcing
-
ACM
-
Matteo Venanzi, John Guiver, Gabriella Kazai, Pushmeet Kohli, and Milad Shokouhi. 2014. Community-based bayesian aggregation models for crowdsourcing. In Proceedings of the 23rd international conference on World wide web. ACM, pages 155-164.
-
(2014)
Proceedings of The 23rd International Conference on World Wide Web
, pp. 155-164
-
-
Venanzi, M.1
Guiver, J.2
Kazai, G.3
Kohli, P.4
Shokouhi, M.5
-
39
-
-
84989205130
-
Extracting pico sentences from clinical trial reports using supervised distant supervision
-
Byron C Wallace, Joël Kuiper, Aakash Sharma, Mingxi Brian Zhu, and Iain J Marshall. 2016. Extracting pico sentences from clinical trial reports using supervised distant supervision. Journal of Machine Learning Research 17(132):1-25.
-
(2016)
Journal of Machine Learning Research
, vol.17
, Issue.132
, pp. 1-25
-
-
Wallace, B.C.1
Kuiper, J.2
Sharma, A.3
Zhu, M.B.4
Marshall, I.J.5
|