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Conference on Natural Language Learning (CoNLL)

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2010 Proceedings of the Fourteenth Conference on Computational Natural Language Learning
Proceedings of the Fourteenth Conference on Computational Natural Language Learning – Shared Task

2010

  1. Proceedings of the Fourteenth Conference on Computational Natural Language Learning

  2. W10-29 [bib]: Entire Volume
  3. W10-2900 [bib]: Front Matter

  4. W10-2901 [bib]: Christian Hänig
    Improvements in Unsupervised Co-Occurrence Based Parsing
  5. W10-2902 [bib]: Valentin I. Spitkovsky; Hiyan Alshawi; Daniel Jurafsky; Christopher D. Manning
    Viterbi Training Improves Unsupervised Dependency Parsing
  6. W10-2903 [bib]: James Clarke; Dan Goldwasser; Ming-Wei Chang; Dan Roth
    Driving Semantic Parsing from the World’s Response
  7. W10-2904 [bib]: Alexander Clark
    Efficient, Correct, Unsupervised Learning for Context-Sensitive Languages
  8. W10-2905 [bib]: Jesús Santamaría; Lourdes Araujo
    Identifying Patterns for Unsupervised Grammar Induction
  9. W10-2906 [bib]: David Burkett; Slav Petrov; John Blitzer; Dan Klein
    Learning Better Monolingual Models with Unannotated Bilingual Text
  10. W10-2907 [bib]: Lillian Lee
    (Invited Talk) Clueless: Explorations in Unsupervised, Knowledge-Lean Extraction of Lexical-Semantic Information
  11. W10-2908 [bib]: Zoubin Ghahramani
    (Invited Talk) Bayesian Hidden Markov Models and Extensions
  12. W10-2909 [bib]: Roi Reichart; Raanan Fattal; Ari Rappoport
    Improved Unsupervised POS Induction Using Intrinsic Clustering Quality and a Zipfian Constraint
  13. W10-2910 [bib]: Richard Johansson; Alessandro Moschitti
    Syntactic and Semantic Structure for Opinion Expression Detection
  14. W10-2911 [bib]: Roi Reichart; Omri Abend; Ari Rappoport
    Type Level Clustering Evaluation: New Measures and a POS Induction Case Study
  15. W10-2912 [bib]: Constantine Lignos; Charles Yang
    Recession Segmentation: Simpler Online Word Segmentation Using Limited Resources
  16. W10-2913 [bib]: Thomas Schoenemann
    Computing Optimal Alignments for the IBM-3 Translation Model
  17. W10-2914 [bib]: Dmitry Davidov; Oren Tsur; Ari Rappoport
    Semi-Supervised Recognition of Sarcasm in Twitter and Amazon
  18. W10-2915 [bib]: Markos Mylonakis; Khalil Sima’an
    Learning Probabilistic Synchronous CFGs for Phrase-Based Translation
  19. W10-2916 [bib]: Sankaranarayanan Ananthakrishnan; Rohit Prasad; David Stallard; Prem Natarajan
    A Semi-Supervised Batch-Mode Active Learning Strategy for Improved Statistical Machine Translation
  20. W10-2917 [bib]: Shujian Huang; Kangxi Li; Xinyu Dai; Jiajun Chen
    Improving Word Alignment by Semi-Supervised Ensemble
  21. W10-2918 [bib]: Chenghua Lin; Yulan He; Richard Everson
    A Comparative Study of Bayesian Models for Unsupervised Sentiment Detection
  22. W10-2919 [bib]: Jorge Carrillo de Albornoz; Laura Plaza; Pablo Gervás
    A Hybrid Approach to Emotional Sentence Polarity and Intensity Classification
  23. W10-2920 [bib]: Micah Hodosh; Peter Young; Cyrus Rashtchian; Julia Hockenmaier
    Cross-Caption Coreference Resolution for Automatic Image Understanding
  24. W10-2921 [bib]: Shane Bergsma; Dekang Lin; Dale Schuurmans
    Improved Natural Language Learning via Variance-Regularization Support Vector Machines
  25. W10-2922 [bib]: Grzegorz Chrupała; Afra Alishahi
    Online Entropy-Based Model of Lexical Category Acquisition
  26. W10-2923 [bib]: Su Nam Kim; Li Wang; Timothy Baldwin
    Tagging and Linking Web Forum Posts
  27. W10-2924 [bib]: Rohit J. Kate; Raymond Mooney
    Joint Entity and Relation Extraction Using Card-Pyramid Parsing
  28. W10-2925 [bib]: Kevin Gimpel; Dipanjan Das; Noah A. Smith
    Distributed Asynchronous Online Learning for Natural Language Processing
  29. W10-2926 [bib]: Daniele Pighin; Alessandro Moschitti
    On Reverse Feature Engineering of Syntactic Tree Kernels
  30. W10-2927 [bib]: Yoav Goldberg; Michael Elhadad
    Inspecting the Structural Biases of Dependency Parsing Algorithms
  31. Proceedings of the Fourteenth Conference on Computational Natural Language Learning – Shared Task

  32. W10-30 [bib]: Entire Volume
  33. W10-3000 [bib]: Front Matter

  34. W10-3001 [bib]: Richárd Farkas; Veronika Vincze; György Móra; János Csirik; György Szarvas
    The CoNLL-2010 Shared Task: Learning to Detect Hedges and their Scope in Natural Language Text
  35. W10-3002 [bib]: Buzhou Tang; Xiaolong Wang; Xuan Wang; Bo Yuan; Shixi Fan
    A Cascade Method for Detecting Hedges and their Scope in Natural Language Text
  36. W10-3003 [bib]: Andreas Vlachos; Mark Craven
    Detecting Speculative Language Using Syntactic Dependencies and Logistic Regression
  37. W10-3004 [bib]: Maria Georgescul
    A Hedgehop over a Max-Margin Framework Using Hedge Cues
  38. W10-3005 [bib]: Feng Ji; Xipeng Qiu; Xuanjing Huang
    Detecting Hedge Cues and their Scopes with Average Perceptron
  39. W10-3006 [bib]: Roser Morante; Vincent Van Asch; Walter Daelemans
    Memory-Based Resolution of In-Sentence Scopes of Hedge Cues
  40. W10-3007 [bib]: Erik Velldal; Lilja Øvrelid; Stephan Oepen
    Resolving Speculation: MaxEnt Cue Classification and Dependency-Based Scope Rules
  41. W10-3008 [bib]: Marek Rei; Ted Briscoe
    Combining Manual Rules and Supervised Learning for Hedge Cue and Scope Detection
  42. W10-3009 [bib]: Eraldo Fernandes; Carlos Crestana; Ruy Milidiú
    Hedge Detection Using the RelHunter Approach
  43. W10-3010 [bib]: Halil Kilicoglu; Sabine Bergler
    A High-Precision Approach to Detecting Hedges and their Scopes
  44. W10-3011 [bib]: Xinxin Li; Jianping Shen; Xiang Gao; Xuan Wang
    Exploiting Rich Features for Detecting Hedges and their Scope
  45. W10-3012 [bib]: Oscar Täckström; Sumithra Velupillai; Martin Hassel; Gunnar Eriksson; Hercules Dalianis; Jussi Karlgren
    Uncertainty Detection as Approximate Max-Margin Sequence Labelling
  46. W10-3013 [bib]: Shaodian Zhang; Hai Zhao; Guodong Zhou; Bao-Liang Lu
    Hedge Detection and Scope Finding by Sequence Labeling with Procedural Feature Selection
  47. W10-3014 [bib]: Qi Zhao; Chengjie Sun; Bingquan Liu; Yong Cheng
    Learning to Detect Hedges and their Scope Using CRF
  48. W10-3015 [bib]: Huiwei Zhou; Xiaoyan Li; Degen Huang; Zezhong Li; Yuansheng Yang
    Exploiting Multi-Features to Detect Hedges and their Scope in Biomedical Texts
  49. W10-3016 [bib]: Lin Chen; Barbara Di Eugenio
    A Lucene and Maximum Entropy Model Based Hedge Detection System
  50. W10-3017 [bib]: David Clausen
    HedgeHunter: A System for Hedge Detection and Uncertainty Classification
  51. W10-3018 [bib]: Liliana Mamani Sánchez; Baoli Li; Carl Vogel
    Exploiting CCG Structures with Tree Kernels for Speculation Detection
  52. W10-3019 [bib]: Vinodkumar Prabhakaran
    Uncertainty Learning Using SVMs and CRFs
  53. W10-3020 [bib]: Nobuyuki Shimizu; Hiroshi Nakagawa
    Features for Detecting Hedge Cues
  54. W10-3021 [bib]: Ferenc Szidarovszky; Illés Solt; Domonkos Tikk
    A Simple Ensemble Method for Hedge Identification
  55. W10-3022 [bib]: Erik Tjong Kim Sang
    A Baseline Approach for Detecting Sentences Containing Uncertainty
  56. W10-3023 [bib]: Yi Zheng; Qifeng Dai; Qiming Luo; Enhong Chen
    Hedge Classification with Syntactic Dependency Features Based on an Ensemble Classifier