📰 CoNLL 2010: all accepted papers

50 papers · the most cited first · ← CoNLL 2010

1. (Invited Talk) Bayesian Hidden Markov Models and Extensions ↗Zoubin Ghahramani
2. (Invited Talk) Clueless: Explorations in Unsupervised, Knowledge-Lean Extraction of Lexical-Semantic Information ↗Lillian Lee
3. A Baseline Approach for Detecting Sentences Containing Uncertainty ↗Erik Tjong Kim Sang
4. A Cascade Method for Detecting Hedges and their Scope in Natural Language TextBuzhou Tang, Xiaolong Wang, Xuan Wang et al.our summary
5. A Comparative Study of Bayesian Models for Unsupervised Sentiment Detection ↗Chenghua Lin, Yulan He, Richard Everson
6. A Hedgehop over a Max-Margin Framework Using Hedge CuesMaria Georgesculour summary
7. A High-Precision Approach to Detecting Hedges and their ScopesHalil Kilicoglu, Sabine Berglerour summary
8. A Hybrid Approach to Emotional Sentence Polarity and Intensity Classification ↗Jorge Carrillo de Albornoz, Laura Plaza, Pablo Gervás
9. A Lucene and Maximum Entropy Model Based Hedge Detection SystemLin Chen, Barbara Di Eugenioour summary
10. A Semi-Supervised Batch-Mode Active Learning Strategy for Improved Statistical Machine Translation ↗Sankaranarayanan Ananthakrishnan, Rohit Prasad, David Stallard et al.
11. A Simple Ensemble Method for Hedge IdentificationFerenc Szidarovszky, Illés Solt, Domonkos Tikkour summary
12. Combining Manual Rules and Supervised Learning for Hedge Cue and Scope DetectionMarek Rei, Ted Briscoeour summary
13. Computing Optimal Alignments for the IBM-3 Translation Model ↗Thomas Schoenemann
14. Cross-Caption Coreference Resolution for Automatic Image Understanding ↗Micah Hodosh, Peter Young, Cyrus Rashtchian et al.
15. Detecting Hedge Cues and their Scopes with Average PerceptronFeng Ji, Xipeng Qiu, Xuanjing Huangour summary
16. Detecting Speculative Language Using Syntactic Dependencies and Logistic RegressionAndreas Vlachos, Mark Cravenour summary
17. Distributed Asynchronous Online Learning for Natural Language Processing ↗Kevin Gimpel, Dipanjan Das, Noah A. Smith
18. Driving Semantic Parsing from the World's Response ↗James Clarke, Dan Goldwasser, Ming-Wei Chang et al.
19. Efficient, Correct, Unsupervised Learning for Context-Sensitive Languages ↗Alexander Clark
20. Exploiting CCG Structures with Tree Kernels for Speculation DetectionLiliana Mamani Sánchez, Baoli Li, Carl Vogelour summary
21. Exploiting Multi-Features to Detect Hedges and their Scope in Biomedical Texts ↗Huiwei Zhou, Xiaoyan Li, Degen Huang et al.
22. Exploiting Rich Features for Detecting Hedges and their ScopeXinxin Li, Jianping Shen, Xiang Gao et al.our summary
23. Features for Detecting Hedge CuesNobuyuki Shimizu, Hiroshi Nakagawaour summary
24. Hedge Classification with Syntactic Dependency Features Based on an Ensemble Classifier ↗Yi Zheng, Qifeng Dai, Qiming Luo et al.
25. Hedge Detection Using the RelHunter ApproachEraldo Fernandes, Carlos Crestana, Ruy Milidiúour summary
26. Hedge Detection and Scope Finding by Sequence Labeling with Procedural Feature Selection ↗Shaodian Zhang, Hai Zhao, Guodong Zhou et al.
27. HedgeHunter: A System for Hedge Detection and Uncertainty ClassificationDavid Clausenour summary
28. Identifying Patterns for Unsupervised Grammar Induction ↗Jesús Santamaría, Lourdes Araujo
29. Improved Natural Language Learning via Variance-Regularization Support Vector Machines ↗Shane Bergsma, Dekang Lin, Dale Schuurmans
30. Improved Unsupervised POS Induction Using Intrinsic Clustering Quality and a Zipfian Constraint ↗Roi Reichart, Raanan Fattal, Ari Rappoport
31. Improvements in Unsupervised Co-Occurrence Based Parsing ↗Christian Hänig
32. Improving Word Alignment by Semi-Supervised Ensemble ↗Shujian Huang, Kangxi Li, Xinyu Dai et al.
33. Inspecting the Structural Biases of Dependency Parsing Algorithms ↗Yoav Goldberg, Michael Elhadad
34. Joint Entity and Relation Extraction Using Card-Pyramid Parsing ↗Rohit J. Kate, Raymond Mooney
35. Learning Better Monolingual Models with Unannotated Bilingual Text ↗David Burkett, Slav Petrov, John Blitzer et al.
36. Learning Probabilistic Synchronous CFGs for Phrase-Based Translation ↗Markos Mylonakis, Khalil Sima'an
37. Learning to Detect Hedges and their Scope Using CRF ↗Qi Zhao, Chengjie Sun, Bingquan Liu et al.
38. Memory-Based Resolution of In-Sentence Scopes of Hedge CuesRoser Morante, Vincent Van Asch, Walter Daelemansour summary
39. On Reverse Feature Engineering of Syntactic Tree Kernels ↗Daniele Pighin, Alessandro Moschitti
40. Online Entropy-Based Model of Lexical Category Acquisition ↗Grzegorz Chrupała, Afra Alishahi
41. Recession Segmentation: Simpler Online Word Segmentation Using Limited Resources ↗Constantine Lignos, Charles Yang
42. Resolving Speculation: MaxEnt Cue Classification and Dependency-Based Scope Rules ↗Erik Velldal, Lilja Øvrelid, Stephan Oepen
43. Semi-Supervised Recognition of Sarcasm in Twitter and AmazonDmitry Davidov, Oren Tsur, Ari Rappoportour summary
44. Syntactic and Semantic Structure for Opinion Expression Detection ↗Richard Johansson, Alessandro Moschitti
45. Tagging and Linking Web Forum Posts ↗Su Nam Kim, Li Wang, Timothy Baldwin
46. The CoNLL-2010 Shared Task: Learning to Detect Hedges and their Scope in Natural Language TextRichárd Farkas, Veronika Vincze, György Móra et al.our summary
47. Type Level Clustering Evaluation: New Measures and a POS Induction Case Study ↗Roi Reichart, Omri Abend, Ari Rappoport
48. Uncertainty Detection as Approximate Max-Margin Sequence LabellingOscar Täckström, Sumithra Velupillai, Martin Hassel et al.our summary
49. Uncertainty Learning Using SVMs and CRFs ↗Vinodkumar Prabhakaran
50. Viterbi Training Improves Unsupervised Dependency Parsing ↗Valentin I. Spitkovsky, Hiyan Alshawi, Daniel Jurafsky et al.

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