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*SEM 2013: all accepted papers

113 papers · page 1 of 2 · the most cited first · ← *SEM 2013

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  1. AI-KU: Using Substitute Vectors and Co-Occurrence Modeling For Word Sense Induction and DisambiguationOsman Başkaya, Enis Sert, Volkan Cirik et al.
  2. ALTN: Word Alignment Features for Cross-lingual Textual EntailmentMarco Turchi, Matteo Negri
  3. AMI&ERIC: How to Learn with Naive Bayes and Prior Knowledge: an Application to Sentiment AnalysisMohamed Dermouche, Leila Khouas, Julien Velcin et al.
  4. ASVUniOfLeipzig: Sentiment Analysis in Twitter using Data-driven Machine Learning TechniquesRobert Remus
  5. ATT1: Temporal Annotation Using Big Windows and Rich Syntactic and Semantic FeaturesHyuckchul Jung, Amanda Stent
  6. AVAYA: Sentiment Analysis on Twitter with Self-Training and Polarity Lexicon ExpansionLee Becker, George Erhart, David Skiba et al.
  7. BOUNCE: Sentiment Classification in Twitter using Rich Feature SetsNadin Kökciyan, Arda Çelebi, Arzucan Özgür et al.
  8. BUAP: N-gram based Feature Evaluation for the Cross-Lingual Textual Entailment TaskDarnes Vilariño, David Pinto, Saúl León et al.
  9. CNGL: Grading Student Answers by Acts of TranslationErgun Biçici, Josef van Genabith
  10. CU : Computational Assessment of Short Free Text Answers - A Tool for Evaluating Students' UnderstandingIfeyinwa Okoye, Steven Bethard, Tamara Sumner
  11. Celi: EDITS and Generic Text Pair ClassificationMilen Kouylekov, Luca Dini, Alessio Bosca et al.
  12. ClaC: Semantic Relatedness of Words and PhrasesReda Siblini, Leila Kosseim
  13. ClearTK-TimeML: A minimalist approach to TempEval 2013Steven Bethardour summary
  14. CoMeT: Integrating different levels of linguistic modeling for meaning assessmentNiels Ott, Ramon Ziai, Michael Hahn et al.
  15. CodeX: Combining an SVM Classifier and Character N-gram Language Models for Sentiment Analysis on Twitter TextQi Han, Junfei Guo, Hinrich Schuetze
  16. Columbia NLP: Sentiment Detection of Subjective Phrases in Social MediaSara Rosenthal, Kathy McKeown
  17. DAEBAK!: Peripheral Diversity for Multilingual Word Sense DisambiguationSteve L. Manion, Raazesh Sainudiin
  18. Duluth : Word Sense Induction Applied to Web Page ClusteringTed Pedersen
  19. ECNUCS: A Surface Information Based System Description of Sentiment Analysis in Twitter in the SemEval-2013 (Task 2)Tiantian Zhu, Fangxi Zhang, Lan Man
  20. ECNUCS: Recognizing Cross-lingual Textual Entailment Using Multiple Text Similarity and Text Difference MeasuresJiang Zhao, Man Lan, Zheng-Yu Niu
  21. EHU-ALM: Similarity-Feature Based Approach for Student Response AnalysisItziar Aldabe, Montse Maritxalar, Oier Lopez de Lacalle
  22. ETS: Domain Adaptation and Stacking for Short Answer ScoringMichael Heilman, Nitin Madnani
  23. Experiments with DBpedia, WordNet and SentiWordNet as resources for sentiment analysis in micro-bloggingHussam Hamdan, Frederic Béchet, Patrice Bellot
  24. FBK-irst : A Multi-Phase Kernel Based Approach for Drug-Drug Interaction Detection and Classification that Exploits Linguistic InformationMd. Faisal Mahbub Chowdhury, Alberto Lavelli
  25. FBK: Sentiment Analysis in Twitter with TweetstedMd. Faisal Mahbub Chowdhury, Marco Guerini, Sara Tonelli et al.
  26. FBM: Combining lexicon-based ML and heuristics for Social Media PolaritiesCarlos Rodríguez-Penagos, Jordi Atserias Batalla, Joan Codina-Filbà et al.
  27. FSS-TimEx for TempEval-3: Extracting Temporal Information from TextVanni Zavarella, Hristo Tanev
  28. GETALP System : Propagation of a Lesk Measure through an Ant Colony AlgorithmDidier Schwab, Andon Tchechmedjiev, Jérôme Goulian et al.
  29. GU-MLT-LT: Sentiment Analysis of Short Messages using Linguistic Features and Stochastic Gradient DescentTobias Günther, Lenz Furrer
  30. HLTDI: CL-WSD Using Markov Random Fields for SemEval-2013 Task 10Alex Rudnick, Can Liu, Michael Gasser
  31. HeidelTime: Tuning English and Developing Spanish Resources for TempEval-3Jannik Strötgen, Julian Zell, Michael Gertz
  32. HsH: Estimating Semantic Similarity of Words and Short Phrases with Frequency Normalized Distance MeasuresChristian Wartena
  33. IIIT-H: A Corpus-Driven Co-occurrence Based Probabilistic Model for Noun Compound ParaphrasingNitesh Surtani, Arpita Batra, Urmi Ghosh et al.
  34. IIRG: A Naive Approach to Evaluating Phrasal SemanticsLorna Byrne, Caroline Fenlon, John Dunnion
  35. IITB-Sentiment-Analysts: Participation in Sentiment Analysis in Twitter SemEval 2013 TaskKaran Chawla, Ankit Ramteke, Pushpak Bhattacharyya
  36. JU_CSE: A CRF Based Approach to Annotation of Temporal Expression, Event and Temporal RelationsAnup Kumar Kolya, Amitava Kundu, Rajdeep Gupta et al.
  37. KLUE: Simple and robust methods for polarity classificationThomas Proisl, Paul Greiner, Stefan Evert et al.
  38. KUL: Data-driven Approach to Temporal Parsing of Newswire ArticlesOleksandr Kolomiyets, Marie-Francine Moens
  39. Kea: Expression-level Sentiment Analysis from Twitter DataAmeeta Agrawal, Aijun An
  40. LASIGE: using Conditional Random Fields and ChEBI ontologyTiago Grego, Francisco Pinto, Francisco M. Couto
  41. LIMSI : Cross-lingual Word Sense Disambiguation using Translation Sense ClusteringMarianna Apidianaki
  42. LIMSIILES: Basic English Substitution for Student Answer Assessment at SemEval 2013Martin Gleize, Brigitte Grau
  43. MELODI: A Supervised Distributional Approach for Free Paraphrasing of Noun CompoundsTim Van de Cruys, Stergos Afantenos, Philippe Muller
  44. MELODI: Semantic Similarity of Words and Compositional Phrases using Latent Vector WeightingTim Van de Cruys, Stergos Afantenos, Philippe Muller
  45. ManTIME: Temporal expression identification and normalization in the TempEval-3 challengeMichele Filannino, Gavin Brown, Goran Nenadic
  46. NILC_USP: A Hybrid System for Sentiment Analysis in Twitter MessagesPedro Balage Filho, Thiago Pardo
  47. NIL_UCM: Extracting Drug-Drug interactions from text through combination of sequence and tree kernelsBehrouz Bokharaeian, Alberto Díaz
  48. NRC-Canada: Building the State-of-the-Art in Sentiment Analysis of TweetsSaif Mohammad, Svetlana Kiritchenko, Xiaodan Zhuour summary
  49. NRC: A Machine Translation Approach to Cross-Lingual Word Sense Disambiguation (SemEval-2013 Task 10)Marine Carpuat
  50. NTNU: Domain Semi-Independent Short Message Sentiment ClassificationØyvind Selmer, Mikael Brevik, Björn Gambäck et al.
  51. NavyTime: Event and Time Ordering from Raw TextNathanael Chambers
  52. OPTWIMA: Comparing Knowledge-rich and Knowledge-poor Approaches for Sentiment Analysis in Short Informal TextsAlexandra Balahur
  53. REACTION: A naive machine learning approach for sentiment classificationSilvio Moreira, João Filgueiras, Bruno Martins et al.
  54. SAIL: A hybrid approach to sentiment analysisNikolaos Malandrakis, Abe Kazemzadeh, Alexandros Potamianos et al.
  55. SATTY : Word Sense Induction Application in Web Search ClusteringSatyabrata Behera, Upasana Gaikwad, Ramakrishna Bairi et al.
  56. SCAI: Extracting drug-drug interactions using a rich feature vectorTamara Bobić, Juliane Fluck, Martin Hofmann-Apitius
  57. SFS-TUE: Compound Paraphrasing with a Language Model and Discriminative RerankingYannick Versley
  58. SINAI: Machine Learning and Emotion of the Crowd for Sentiment Analysis in MicroblogsEugenio Martínez-Cámara, Arturo Montejo-Ráez, M. Teresa Martín-Valdivia et al.
  59. SOFTCARDINALITY: Hierarchical Text Overlap for Student Response AnalysisSergio Jimenez, Claudia Becerra, Alexander Gelbukh
  60. SOFTCARDINALITY: Learning to Identify Directional Cross-Lingual Entailment from Cardinalities and SMTSergio Jimenez, Claudia Becerra, Alexander Gelbukh
  61. SSA-UO: Unsupervised Sentiment Analysis in TwitterReynier Ortega Bueno, Adrian Fonseca Bruzón, Yoan Gutiérrez et al.
  62. SU-Sentilab : A Classification System for Sentiment Analysis in TwitterGizem Gezici, Rahim Dehkharghani, Berrin Yanikoglu et al.
  63. SUTime: Evaluation in TempEval-3Angel Chang, Christopher D. Manning
  64. SZTE-NLP: Sentiment Detection on Twitter MessagesViktor Hangya, Gábor Berend, Richárd Farkas
  65. SemEval-2013 Task 10: Cross-lingual Word Sense DisambiguationEls Lefever, Véronique Hoste
  66. SemEval-2013 Task 11: Word Sense Induction and Disambiguation within an End-User ApplicationRoberto Navigli, Daniele Vannella
  67. SemEval-2013 Task 12: Multilingual Word Sense DisambiguationRoberto Navigli, David Jurgens, Daniele Vannella
  68. SemEval-2013 Task 13: Word Sense Induction for Graded and Non-Graded SensesDavid Jurgens, Ioannis Klapaftis
  69. SemEval-2013 Task 1: TempEval-3: Evaluating Time Expressions, Events, and Temporal RelationsNaushad UzZaman, Hector Llorens, Leon Derczynski et al.
  70. SemEval-2013 Task 2: Sentiment Analysis in TwitterPreslav Nakov, Sara Rosenthal, Zornitsa Kozareva et al.our summary
  71. SemEval-2013 Task 3: Spatial Role LabelingOleksandr Kolomiyets, Parisa Kordjamshidi, Marie-Francine Moens et al.
  72. SemEval-2013 Task 4: Free Paraphrases of Noun CompoundsIris Hendrickx, Zornitsa Kozareva, Preslav Nakov et al.
  73. SemEval-2013 Task 5: Evaluating Phrasal SemanticsIoannis Korkontzelos, Torsten Zesch, Fabio Massimo Zanzotto et al.
  74. SemEval-2013 Task 7: The Joint Student Response Analysis and 8th Recognizing Textual Entailment ChallengeMyroslava Dzikovska, Rodney Nielsen, Chris Brew et al.our summary
  75. SemEval-2013 Task 9 : Extraction of Drug-Drug Interactions from Biomedical Texts (DDIExtraction 2013)Isabel Segura-Bedmar, Paloma Martínez, María Herrero-Zazoour summary
  76. Semeval-2013 Task 8: Cross-lingual Textual Entailment for Content SynchronizationMatteo Negri, Alessandro Marchetti, Yashar Mehdad et al.
  77. Serendio: Simple and Practical lexicon based approach to Sentiment AnalysisPrabu Palanisamy, Vineet Yadav, Harsha Elchuri
  78. SwatCS: Combining simple classifiers with estimated accuracySam Clark, Rich Wicentwoski
  79. TJP: Using Twitter to Analyze the Polarity of ContextsTawunrat Chalothorn, Jeremy Ellman
  80. UC3M: A kernel-based approach to identify and classify DDIs in bio-medical texts.Daniel Sanchez-Cisneros
  81. UColorado_SOM: Extraction of Drug-Drug Interactions from Biomedical Text using Knowledge-rich and Knowledge-poor FeaturesNegacy Hailu, Lawrence E. Hunter, K. Bretonnel Cohen
  82. UEM-UC3M: An Ontology-based named entity recognition system for biomedical texts.Daniel Sanchez-Cisneros, Fernando Aparicio Gali
  83. UKP-BIU: Similarity and Entailment Metrics for Student Response AnalysisOmer Levy, Torsten Zesch, Ido Dagan et al.
  84. UKP-WSI: UKP Lab Semeval-2013 Task 11 System DescriptionHans-Peter Zorn, Iryna Gurevych
  85. UMCC_DLSI-(EPS): Paraphrases Detection Based on Semantic DistanceHéctor Dávila, Antonio Fernández Orquín, Alexander Chávez et al.
  86. UMCC_DLSI-(SA): Using a ranking algorithm and informal features to solve Sentiment Analysis in TwitterYoan Gutiérrez, Andy González, Roger Pérez et al.
  87. UMCC_DLSI: Reinforcing a Ranking Algorithm with Sense Frequencies and Multidimensional Semantic Resources to solve Multilingual Word Sense DisambiguationYoan Gutiérrez, Yenier Castañeda, Andy González et al.
  88. UMCC_DLSI: Semantic and Lexical features for detection and classification Drugs in biomedical textsArmando Collazo, Alberto Ceballo, Dennys D. Puig et al.
  89. UNAL: Discriminating between Literal and Figurative Phrasal Usage Using Distributional Statistics and POS tagsSergio Jimenez, Claudia Becerra, Alexander Gelbukh
  90. UNITOR-HMM-TK: Structured Kernel-based learning for Spatial Role LabelingEmanuele Bastianelli, Danilo Croce, Roberto Basili et al.
  91. UNITOR: Combining Syntactic and Semantic Kernels for Twitter Sentiment AnalysisGiuseppe Castellucci, Simone Filice, Danilo Croce et al.
  92. USNA: A Dual-Classifier Approach to Contextual Sentiment AnalysisGanesh Harihara, Eugene Yang, Nathanael Chambers
  93. UT-DB: An Experimental Study on Sentiment Analysis in TwitterZhemin Zhu, Djoerd Hiemstra, Peter Apers et al.
  94. UTTime: Temporal Relation Classification using Deep Syntactic FeaturesNatsuda Laokulrat, Makoto Miwa, Yoshimasa Tsuruoka et al.
  95. UTurku: Drug Named Entity Recognition and Drug-Drug Interaction Extraction Using SVM Classification and Domain KnowledgeJari Björne, Suwisa Kaewphan, Tapio Salakoski
  96. UWM-TRIADS: Classifying Drug-Drug Interactions with Two-Stage SVM and Post-ProcessingMajid Rastegar-Mojarad, Richard D. Boyce, Rashmi Prasad
  97. Umelb: Cross-lingual Textual Entailment with Word Alignment and String Similarity FeaturesYvette Graham, Bahar Salehi, Timothy Baldwin
  98. Umigon: sentiment analysis for tweets based on terms lists and heuristicsClement Levallois
  99. UoM: Using Explicit Semantic Analysis for Classifying SentimentsSapna Negi, Michael Rosner
  100. UoS: A Graph-Based System for Graded Word Sense InductionDavid Hope, Bill Keller

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