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SemEval 2014: all accepted papers

149 papers · page 1 of 2 · the most cited first · ← SemEval 2014

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  1. AI-KU: Using Co-Occurrence Modeling for Semantic Similarity ↗Osman Başkaya
  2. ASAP: Automatic Semantic Alignment for Phrases ↗Ana Alves, Adriana Ferrugento, Mariana Lourenço et al.
  3. AT&T: The Tag&Parse Approach to Semantic Parsing of Robot Spatial Commands ↗Svetlana Stoyanchev, Hyuckchul Jung, John Chen et al.
  4. AUEB: Two Stage Sentiment Analysis of Social Network Messages ↗Rafael Michael Karampatsis, John Pavlopoulos, Prodromos Malakasiotis
  5. Alpage: Transition-based Semantic Graph Parsing with Syntactic Features ↗Corentin Ribeyre, Eric Villemonte de la Clergerie, Djamé Seddah
  6. BUAP: Evaluating Compositional Distributional Semantic Models on Full Sentences through Semantic Relatedness and Textual EntailmentSaúl León, Darnes Vilariño, David Pinto et al.our summary
  7. BUAP: Evaluating Features for Multilingual and Cross-Level Semantic Textual Similarity ↗Darnes Vilariño, David Pinto, Saúl León et al.
  8. BUAP: Polarity Classification of Short Texts ↗David Pinto, Darnes Vilariño, Saul León et al.
  9. Bielefeld SC: Orthonormal Topic Modelling for Grammar Induction ↗John P. McCrae, Philipp Cimiano
  10. Biocom Usp: Tweet Sentiment Analysis with Adaptive Boosting Ensemble ↗Nádia Silva, Estevam Hruschka, Eduardo Hruschka
  11. Biocom Usp: Tweet Sentiment Analysis with Adaptive Boosting Ensemble ↗Nádia Silva, Estevam Hruschka, Eduardo Hruschka
  12. BioinformaticsUA: Concept Recognition in Clinical Narratives Using a Modular and Highly Efficient Text Processing Framework ↗Sérgio Matos, Tiago Nunes, José Luís Oliveira
  13. Blinov: Distributed Representations of Words for Aspect-Based Sentiment Analysis at SemEval 2014 ↗Pavel Blinov, Eugeny Kotelnikov
  14. CECL: a New Baseline and a Non-Compositional Approach for the Sick Benchmark ↗Yves Bestgen
  15. CISUC-KIS: Tackling Message Polarity Classification with a Large and Diverse Set of Features ↗João Leal, Sara Pinto, Ana Bento et al.
  16. CMU: Arc-Factored, Discriminative Semantic Dependency Parsing ↗Sam Thomson, Brendan O'Connor, Jeffrey Flanigan et al.
  17. CMUQ-Hybrid: Sentiment Classification By Feature Engineering and Parameter Tuning ↗Kamla Al-Mannai, Hanan Alshikhabobakr, Sabih Bin Wasi et al.
  18. CMUQ@Qatar:Using Rich Lexical Features for Sentiment Analysis on Twitter ↗Sabih Bin Wasi, Rukhsar Neyaz, Houda Bouamor et al.
  19. CNRC-TMT: Second Language Writing Assistant System Description ↗Cyril Goutte, Michel Simard, Marine Carpuat
  20. COMMIT-P1WP3: A Co-occurrence Based Approach to Aspect-Level Sentiment Analysis ↗Kim Schouten, Flavius Frasincar, Franciska de Jong
  21. Citius: A Naive-Bayes Strategy for Sentiment Analysis on English Tweets ↗Pablo Gamallo, Marcos Garcia
  22. Columbia NLP: Sentiment Detection of Sentences and Subjective Phrases in Social Media ↗Sara Rosenthal, Kathy McKeown, Apoorv Agarwal
  23. Coooolll: A Deep Learning System for Twitter Sentiment Classification ↗Duyu Tang, Furu Wei, Bing Qin et al.
  24. Copenhagen-Malmö: Tree Approximations of Semantic Parsing Problems ↗Natalie Schluter, Anders Søgaard, Jakob Elming et al.
  25. DAEDALUS at SemEval-2014 Task 9: Comparing Approaches for Sentiment Analysis in Twitter ↗Julio Villena-Román, Janine García-Morera, José Carlos González-Cristóbal
  26. DCU: Aspect-based Polarity Classification for SemEval Task 4 ↗Joachim Wagner, Piyush Arora, Santiago Cortes et al.
  27. DIT: Summarisation and Semantic Expansion in Evaluating Semantic Similarity ↗Magdalena Kacmajor, John D. Kelleher
  28. DLIREC: Aspect Term Extraction and Term Polarity Classification System ↗Zhiqiang Toh, Wenting Wang
  29. DLS@CU: Sentence Similarity from Word Alignment ↗Md Arafat Sultan, Steven Bethard, Tamara Sumner
  30. Duluth : Measuring Cross-Level Semantic Similarity with First and Second Order Dictionary Overlaps ↗Ted Pedersen
  31. ECNU: A Combination Method and Multiple Features for Aspect Extraction and Sentiment Polarity Classification ↗Fangxi Zhang, Zhihua Zhang, Man Lan
  32. ECNU: Expression- and Message-level Sentiment Orientation Classification in Twitter Using Multiple Effective Features ↗Jiang Zhao, Man Lan, Tiantian Zhu
  33. ECNU: Leveraging on Ensemble of Heterogeneous Features and Information Enrichment for Cross Level Semantic Similarity Estimation ↗Tiantian Zhu, Man Lan
  34. ECNU: One Stone Two Birds: Ensemble of Heterogenous Measures for Semantic Relatedness and Textual Entailment ↗Jiang Zhao, Tiantian Zhu, Man Lan
  35. FBK-TR: Applying SVM with Multiple Linguistic Features for Cross-Level Semantic Similarity ↗Ngoc Phuoc An Vo, Tommaso Caselli, Octavian Popescu
  36. FBK-TR: SVM for Semantic Relatedeness and Corpus Patterns for RTENgoc Phuoc An Vo, Octavian Popescu, Tommaso Caselliour summary
  37. GPLSI: Supervised Sentiment Analysis in Twitter using Skipgrams ↗Javi Fernández, Yoan Gutiérrez, Jose Manuel Gómez et al.
  38. HulTech: A General Purpose System for Cross-Level Semantic Similarity based on Anchor Web Counts ↗Jose G. Moreno, Rumen Moraliyski, Asma Berrezoug et al.
  39. IHS R&D Belarus: Cross-domain extraction of product features using CRF ↗Maryna Chernyshevich
  40. IITP: A Supervised Approach for Disorder Mention Detection and Disambiguation ↗Utpal Kumar Sikdar, Asif Ekbal, Sriparna Saha
  41. IITP: Supervised Machine Learning for Aspect based Sentiment Analysis ↗Deepak Kumar Gupta, Asif Ekbal
  42. IITPatna: Supervised Approach for Sentiment Analysis in Twitter ↗Raja Selvarajan, Asif Ekbal
  43. INSIGHT Galway: Syntactic and Lexical Features for Aspect Based Sentiment Analysis ↗Sapna Negi, Paul Buitelaar
  44. IUCL: Combining Information Sources for SemEval Task 5 ↗Alex Rudnick, Levi King, Can Liu et al.
  45. Illinois-LH: A Denotational and Distributional Approach to Semantics ↗Alice Lai, Julia Hockenmaier
  46. In-House: An Ensemble of Pre-Existing Off-the-Shelf Parsers ↗Yusuke Miyao, Stephan Oepen, Daniel Zeman
  47. Indian Institute of Technology-Patna: Sentiment Analysis in Twitter ↗Vikram Singh, Arif Md. Khan, Asif Ekbal
  48. IxaMed: Applying Freeling and a Perceptron Sequential Tagger at the Shared Task on Analyzing Clinical Texts ↗Koldo Gojenola, Maite Oronoz, Alicia Pérez et al.
  49. JOINT_FORCES: Unite Competing Sentiment Classifiers with Random Forest ↗Oliver Dürr, Fatih Uzdilli, Mark Cieliebak
  50. JU-Evora: A Graph Based Cross-Level Semantic Similarity Analysis using Discourse Information ↗Swarnendu Ghosh, Nibaran Das, Teresa Gonçalves et al.
  51. JU_CSE: A Conditional Random Field (CRF) Based Approach to Aspect Based Sentiment Analysis ↗Braja Gopal Patra, Soumik Mandal, Dipankar Das et al.
  52. KUL-Eval: A Combinatory Categorial Grammar Approach for Improving Semantic Parsing of Robot Commands using Spatial Context ↗Willem Mattelaer, Mathias Verbeke, Davide Nitti
  53. KUNLPLab:Sentiment Analysis on Twitter Data ↗Beakal Gizachew Assefa
  54. Kea: Sentiment Analysis of Phrases Within Short Texts ↗Ameeta Agrawal, Aijun An
  55. LIPN: Introducing a new Geographical Context Similarity Measure and a Statistical Similarity Measure based on the Bhattacharyya coefficient ↗Davide Buscaldi, Jorge García Flores, Joseph Le Roux et al.
  56. LT3: Sentiment Classification in User-Generated Content Using a Rich Feature Set ↗Cynthia Van Hee, Marjan Van de Kauter, Orphée De Clercq et al.
  57. Linköping: Cubic-Time Graph Parsing with a Simple Scoring Scheme ↗Marco Kuhlmann
  58. LyS: Porting a Twitter Sentiment Analysis Approach from Spanish to English ↗David Vilares, Miguel Hermo, Miguel A. Alonso et al.
  59. Meerkat Mafia: Multilingual and Cross-Level Semantic Textual Similarity Systems ↗Abhay Kashyap, Lushan Han, Roberto Yus et al.
  60. MindLab-UNAL: Comparing Metamap and T-mapper for Medical Concept Extraction in SemEval 2014 Task 7 ↗Alejandro Riveros, Maria De-Arteaga, Fabio González et al.
  61. NILC_USP: An Improved Hybrid System for Sentiment Analysis in Twitter Messages ↗Pedro Balage Filho, Lucas Avanço, Thiago Pardo et al.
  62. NILC_USP: Aspect Extraction using Semantic Labels ↗Pedro Balage Filho, Thiago Pardo
  63. NRC-Canada-2014: Detecting Aspects and Sentiment in Customer ReviewsSvetlana Kiritchenko, Xiaodan Zhu, Colin Cherry et al.our summary
  64. NRC-Canada-2014: Recent Improvements in the Sentiment Analysis of Tweets ↗Xiaodan Zhu, Svetlana Kiritchenko, Saif Mohammad
  65. NTNU: Measuring Semantic Similarity with Sublexical Feature Representations and Soft Cardinality ↗André Lynum, Partha Pakray, Björn Gambäck et al.
  66. OPI: Semeval-2014 Task 3 System Description ↗Marek Kozlowski
  67. Peking: Profiling Syntactic Tree Parsing Techniques for Semantic Graph Parsing ↗Yantao Du, Fan Zhang, Weiwei Sun et al.
  68. Potsdam: Semantic Dependency Parsing by Bidirectional Graph-Tree Transformations and Syntactic Parsing ↗Željko Agić, Alexander Koller
  69. Priberam: A Turbo Semantic Parser with Second Order Features ↗André F. T. Martins, Mariana S. C. Almeida
  70. RTM-DCU: Referential Translation Machines for Semantic Similarity ↗Ergun Biçici, Andy Way
  71. RTRGO: Enhancing the GU-MLT-LT System for Sentiment Analysis of Short Messages ↗Tobias Günther, Jean Vancoppenolle, Richard Johansson
  72. RelAgent: Entity Detection and Normalization for Diseases in Clinical Records: a Linguistically Driven Approach ↗Sv Ramanan, Senthil Nathan
  73. RoBox: CCG with Structured Perceptron for Supervised Semantic Parsing of Robotic Spatial Commands ↗Kilian Evang, Johan Bos
  74. SA-UZH: Verb-based Sentiment Analysis ↗Nora Hollenstein, Michael Amsler, Martina Bachmann et al.
  75. SAIL-GRS: Grammar Induction for Spoken Dialogue Systems using CF-IRF Rule Similarity ↗Kalliopi Zervanou, Nikolaos Malandrakis, Shrikanth Narayanan
  76. SAIL: Sentiment Analysis using Semantic Similarity and Contrast Features ↗Nikolaos Malandrakis, Michael Falcone, Colin Vaz et al.
  77. SAP-RI: A Constrained and Supervised Approach for Aspect-Based Sentiment Analysis ↗Naveen Nandan, Daniel Dahlmeier, Akriti Vij et al.
  78. SAP-RI: Twitter Sentiment Analysis in Two Days ↗Akriti Vij, Nishta Malhotra, Naveen Nandan et al.
  79. SINAI: Voting System for Aspect Based Sentiment Analysis ↗Salud María Jiménez-Zafra, Eugenio Martínez-Cámara, Maite Martin et al.
  80. SINAI: Voting System for Twitter Sentiment Analysis ↗Eugenio Martínez-Cámara, Salud María Jiménez-Zafra, Maite Martin et al.
  81. SNAP: A Multi-Stage XML-Pipeline for Aspect Based Sentiment Analysis ↗Clemens Schulze Wettendorf, Robin Jegan, Allan Körner et al.
  82. SSMT:A Machine Translation Evaluation View To Paragraph-to-Sentence Semantic Similarity ↗Pingping Huang, Baobao Chang
  83. SU-FMI: System Description for SemEval-2014 Task 9 on Sentiment Analysis in Twitter ↗Boris Velichkov, Borislav Kapukaranov, Ivan Grozev et al.
  84. SZTE-NLP: Aspect level opinion mining exploiting syntactic cues ↗Viktor Hangya, Gábor Berend, István Varga et al.
  85. SZTE-NLP: Clinical Text Analysis with Named Entity Recognition ↗Melinda Katona, Richárd Farkas
  86. SeemGo: Conditional Random Fields Labeling and Maximum Entropy Classification for Aspect Based Sentiment Analysis ↗Pengfei Liu, Helen Meng
  87. SemEval 2014 Task 5 - L2 Writing Assistant ↗Maarten van Gompel, Iris Hendrickx, Antal van den Bosch et al.
  88. SemEval 2014 Task 8: Broad-Coverage Semantic Dependency Parsing ↗Stephan Oepen, Marco Kuhlmann, Yusuke Miyao et al.
  89. SemEval-2014 Task 10: Multilingual Semantic Textual SimilarityEneko Agirre, Carmen Banea, Claire Cardie et al.our summary
  90. SemEval-2014 Task 1: Evaluation of Compositional Distributional Semantic Models on Full Sentences through Semantic Relatedness and Textual EntailmentMarco Marelli, Luisa Bentivogli, Marco Baroni et al.our summary
  91. SemEval-2014 Task 2: Grammar Induction for Spoken Dialogue Systems ↗Ioannis Klasinas, Elias Iosif, Katerina Louka et al.
  92. SemEval-2014 Task 3: Cross-Level Semantic Similarity ↗David Jurgens, Mohammad Taher Pilehvar, Roberto Navigli
  93. SemEval-2014 Task 4: Aspect Based Sentiment AnalysisMaria Pontiki, Dimitris Galanis, John Pavlopoulos et al.our summary
  94. SemEval-2014 Task 6: Supervised Semantic Parsing of Robotic Spatial Commands ↗Kais Dukes
  95. SemEval-2014 Task 7: Analysis of Clinical Text ↗Sameer Pradhan, Noémie Elhadad, Wendy Chapman et al.
  96. SemEval-2014 Task 9: Sentiment Analysis in TwitterSara Rosenthal, Alan Ritter, Preslav Nakov et al.our summary
  97. SemantiKLUE: Robust Semantic Similarity at Multiple Levels Using Maximum Weight Matching ↗Thomas Proisl, Stefan Evert, Paul Greiner et al.
  98. Sensible: L2 Translation Assistance by Emulating the Manual Post-Editing Process ↗Liling Tan, Anne-Kathrin Schumann, Jose M.M. Martinez et al.
  99. Senti.ue: Tweet Overall Sentiment Classification Approach for SemEval-2014 Task 9 ↗José Saias
  100. SentiKLUE: Updating a Polarity Classifier in 48 Hours ↗Stefan Evert, Thomas Proisl, Paul Greiner et al.

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