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International Workshop on Semantic Evaluations 2007: all accepted papers

110 papers · page 1 of 2 · the most cited first · ← International Workshop on Semantic Evaluations 2007

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  1. AUG: A combined classification and clustering approach for web people disambiguationEls Lefever, Véronique Hoste, Timur Fayruzov
  2. CITYU-HIF: WSD with Human-Informed Feature PreferenceOi Yee Kwong
  3. CLR: Integration of FrameNet in a Text Representation SystemKenneth C. Litkowski
  4. CLaC and CLaC-NB: Knowledge-based and corpus-based approaches to sentiment taggingAlina Andreevskaia, Sabine Bergler
  5. CMU-AT: Semantic Distance and Background Knowledge for Identifying Semantic RelationsAlicia Tribble, Scott E. Fahlman
  6. CU-COMSEM: Exploring Rich Features for Unsupervised Web Personal Name DisambiguationYing Chen, James H. Martin
  7. CU-TMP: Temporal Relation Classification Using Syntactic and Semantic FeaturesSteven Bethard, James H. Martin
  8. CUNIT: A Semantic Role Labeling System for Modern Standard ArabicMona Diab, Alessandro Moschitti, Daniele Pighin
  9. DFKI2: An Information Extraction Based Approach to People DisambiguationAndrea Heyl, Günter Neumann
  10. FBK-IRST: Kernel Methods for Semantic Relation ExtractionClaudio Giuliano, Alberto Lavelli, Daniele Pighin et al.
  11. FBK-irst: Lexical Substitution Task Exploiting Domain and Syntagmatic CoherenceClaudio Giuliano, Alfio Gliozzo, Carlo Strapparava
  12. FICO: Web Person Disambiguation Via Weighted Similarity of Entity ContextsPaul Kalmar, Matthias Blume
  13. FUH (FernUniversität in Hagen): Metonymy Recognition Using Different Kinds of Context for a Memory-Based LearnerJohannes Leveling
  14. GPLSI: Word Coarse-grained Disambiguation aided by Basic Level ConceptsRubén Izquierdo, Armando Suárez, German Rigau
  15. GYDER: Maxent Metonymy ResolutionRichárd Farkas, Eszter Simon, György Szarvas et al.
  16. HIT-IR-WSD: A WSD System for English Lexical Sample TaskYuhang Guo, Wanxiang Che, Yuxuan Hu et al.
  17. HIT-WSD: Using Search Engine for Multilingual Chinese-English Lexical Sample TaskPengYuan Liu, TieJun Zhao, MuYun Yang
  18. HIT: Web based Scoring Method for English Lexical SubstitutionShiqi Zhao, Lin Zhao, Yu Zhang et al.
  19. I2R: Three Systems for Word Sense Discrimination, Chinese Word Sense Disambiguation, and English Word Sense DisambiguationZheng-Yu Niu, Dong-Hong Ji, Chew-Lim Tan
  20. ILK2: Semantic Role Labeling of Catalan and Spanish using TiMBLRoser Morante, Bertjan Busser
  21. ILK: Machine learning of semantic relations with shallow features and almost no dataIris Hendrickx, Roser Morante, Caroline Sporleder et al.
  22. IRST-BP: Preposition Disambiguation based on Chain Clarifying Relationships ContextsOctavian Popescu, Sara Tonelli, Emanuele Pianta
  23. IRST-BP: Web People Search Using Name EntitiesOctavian Popescu, Bernardo Magnini
  24. JHU1 : An Unsupervised Approach to Person Name Disambiguation using Web SnippetsDelip Rao, Nikesh Garera, David Yarowsky
  25. JU-SKNSB: Extended WordNet Based WSD on the English All-Words Task at SemEval-1Sudip Kumar Naskar, Sivaji Bandyopadhyay
  26. KU: Word Sense Disambiguation by SubstitutionDeniz Yuret
  27. LCC-SRN: LCC's SRN System for SemEval 2007 Task 4Adriana Badulescu, Munirathnam Srikanth
  28. LCC-TE: A Hybrid Approach to Temporal Relation Identification in News TextCongmin Min, Munirathnam Srikanth, Abraham Fowler
  29. LCC-WSD: System Description for English Coarse Grained All Words Task at SemEval 2007Adrian Novischi, Muirathnam Srikanth, Andrew Bennett
  30. LTH: Semantic Structure Extraction using Nonprojective Dependency TreesRichard Johansson, Pierre Nugues
  31. MELB-KB: Nominal Classification as Noun Compound InterpretationSu Nam Kim, Timothy Baldwin
  32. MELB-MKB: Lexical Substitution system based on Relatives in ContextDavid Martinez, Su Nam Kim, Timothy Baldwin
  33. MELB-YB: Preposition Sense Disambiguation Using Rich Semantic FeaturesPatrick Ye, Timothy Baldwin
  34. NAIST.Japan: Temporal Relation Identification Using Dependency Parsed TreeYuchang Cheng, Masayuki Asahara, Yuji Matsumoto
  35. NUS-ML:Improving Word Sense Disambiguation Using Topic FeaturesJun Fu Cai, Wee Sun Lee, Yee Whye Teh
  36. NUS-PT: Exploiting Parallel Texts for Word Sense Disambiguation in the English All-Words TasksYee Seng Chan, Hwee Tou Ng, Zhi Zhong
  37. OE: WSD Using Optimal Ensembling (OE) MethodHarri M. T. Saarikoski
  38. PKU: Combining Supervised Classifiers with Features SelectionPeng Jin, Danqing Zhu, Fuxin Li et al.
  39. PNNL: A Supervised Maximum Entropy Approach to Word Sense DisambiguationStephen Tratz, Antonio Sanfilippo, Michelle Gregory et al.
  40. PSNUS: Web People Name Disambiguation by Simple Clustering with Rich FeaturesErgin Elmacioglu, Yee Fan Tan, Su Yan et al.
  41. PU-BCD: Exponential Family Models for the Coarse- and Fine-Grained All-Words TasksJonathan Chang, Miroslav Dudík, David Blei
  42. PUTOP: Turning Predominant Senses into a Topic Model for Word Sense DisambiguationJordan Boyd-Graber, David Blei
  43. RACAI: Meaning Affinity ModelsRadu Ion, Dan Tufiş
  44. RTV: Tree Kernels for Thematic Role ClassificationDaniele Pighin, Alessandro Moschitti, Roberto Basili
  45. SHEF: Semantic Tagging and Summarization Techniques Applied to Cross-document CoreferenceHoracio Saggion
  46. SICS: Valence annotation based on seeds in word spaceMagnus Sahlgren, Jussi Karlgren, Gunnar Eriksson
  47. SRCB-WSD: Supervised Chinese Word Sense Disambiguation with Key FeaturesYun Xing
  48. SW-AG: Local Context Matching for English Lexical SubstitutionGeorge Dahl, Anne-Marie Frassica, Richard Wicentowski
  49. SWAT-MP:The SemEval-2007 Systems for Task 5 and Task 14Phil Katz, Matt Singleton, Richard Wicentowski
  50. SemEval-2007 Task 01: Evaluating WSD on Cross-Language Information RetrievalEneko Agirre, Bernardo Magnini, Oier Lopez de Lacalle et al.
  51. SemEval-2007 Task 02: Evaluating Word Sense Induction and Discrimination SystemsEneko Agirre, Aitor Soroa
  52. SemEval-2007 Task 04: Classification of Semantic Relations between NominalsRoxana Girju, Preslav Nakov, Vivi Nastase et al.our summary
  53. SemEval-2007 Task 05: Multilingual Chinese-English Lexical SamplePeng Jin, Yunfang Wu, Shiwen Yu
  54. SemEval-2007 Task 06: Word-Sense Disambiguation of PrepositionsKenneth C. Litkowski, Orin Hargraves
  55. SemEval-2007 Task 07: Coarse-Grained English All-Words TaskRoberto Navigli, Kenneth C. Litkowski, Orin Hargravesour summary
  56. SemEval-2007 Task 08: Metonymy Resolution at SemEval-2007Katja Markert, Malvina Nissim
  57. SemEval-2007 Task 09: Multilevel Semantic Annotation of Catalan and SpanishLluís Màrquez, Luis Villarejo, M. A. Martí et al.
  58. SemEval-2007 Task 10: English Lexical Substitution TaskDiana McCarthy, Roberto Navigliour summary
  59. SemEval-2007 Task 11: English Lexical Sample Task via English-Chinese Parallel TextHwee Tou Ng, Yee Seng Chan
  60. SemEval-2007 Task 12: Turkish Lexical Sample TaskZeynep Orhan, Emine Çelik, Demirgüç Neslihan
  61. SemEval-2007 Task 14: Affective TextCarlo Strapparava, Rada Mihalceaour summary
  62. SemEval-2007 Task 15: TempEval Temporal Relation IdentificationMarc Verhagen, Robert Gaizauskas, Frank Schilder et al.
  63. SemEval-2007 Task 16: Evaluation of Wide Coverage Knowledge ResourcesMontse Cuadros, German Rigau
  64. SemEval-2007 Task 18: Arabic Semantic LabelingMona Diab, Musa Alkhalifa, Sabry ElKateb et al.
  65. SemEval-2007 Task 19: Frame Semantic Structure ExtractionCollin Baker, Michael Ellsworth, Katrin Erkour summary
  66. SemEval-2007 Task-17: English Lexical Sample, SRL and All WordsSameer Pradhan, Edward Loper, Dmitriy Dligach et al.our summary
  67. Sussx: WSD using Automatically Acquired Predominant SensesRob Koeling, Diana McCarthy
  68. TITPI: Web People Search Task Using Semi-Supervised Clustering ApproachKazunari Sugiyama, Manabu Okumura
  69. TKB-UO: Using Sense Clustering for WSDHenry Anaya-Sánchez, Aurora Pons-Porrata, Rafael Berlanga-Llavori
  70. The SemEval-2007 WePS Evaluation: Establishing a benchmark for the Web People Search TaskJavier Artiles, Julio Gonzalo, Satoshi Sekine
  71. Tor, TorMd: Distributional Profiles of Concepts for Unsupervised Word Sense DisambiguationSaif Mohammad, Graeme Hirst, Philip Resnik
  72. UA-ZBSA: A Headline Emotion Classification through Web InformationZornitsa Kozareva, Borja Navarro, Sonia Vázquez et al.
  73. UA-ZSA: Web Page Clustering on the basis of Name DisambiguationZornitsa Kozareva, Sonia Vazquez, Andres Montoyo
  74. UBC-ALM: Combining k-NN with SVD for WSDEneko Agirre, Oier Lopez de Lacalle
  75. UBC-AS: A Graph Based Unsupervised System for Induction and ClassificationEneko Agirre, Aitor Soroa
  76. UBC-UMB: Combining unsupervised and supervised systems for all-words WSDDavid Martinez, Timothy Baldwin, Eneko Agirre et al.
  77. UBC-UPC: Sequential SRL Using Selectional Preferences. An approach with Maximum Entropy Markov ModelsBeñat Zapirain, Eneko Agirre, Lluís Màrquez
  78. UBC-ZAS: A k-NN based Multiclassifier System to perform WSD in a Reduced Dimensional Vector SpaceAna Zelaia, Olatz Arregi, Basilio Sierra
  79. UC3M_13: Disambiguation of Person Names Based on the Composition of Simple Bags of Typed TermsDavid del Valle-Agudo, César de Pablo-Sánchez, María Teresa Vicente-Díez
  80. UCB: System Description for SemEval Task \#4Preslav Nakov, Marti Hearst
  81. UCD-FC: Deducing semantic relations using WordNet senses that occur frequently in a database of noun-noun compoundsFintan J. Costello
  82. UCD-PN: Classification of Semantic Relations Between Nominals using WordNet and Web CountsPaul Nulty
  83. UCD-S1: A hybrid model for detecting semantic relations between noun pairs in textCristina Butnariu, Tony Veale
  84. UCM3: Classification of Semantic Relations between Nominals using Sequential Minimal OptimizationIsabel Segura Bedmar, Doaa Samy, Jose L. Martinez
  85. UIUC: A Knowledge-rich Approach to Identifying Semantic Relations between NominalsBrandon Beamer, Suma Bhat, Brant Chee et al.
  86. UMND1: Unsupervised Word Sense Disambiguation Using Contextual Semantic RelatednessSiddharth Patwardhan, Satanjeev Banerjee, Ted Pedersen
  87. UMND2 : SenseClusters Applied to the Sense Induction Task of Senseval-4Ted Pedersen
  88. UNIBA: JIGSAW algorithm for Word Sense DisambiguationPierpaolo Basile, Marco de Gemmis, Anna Lisa Gentile et al.
  89. UNN-WePS: Web Person Search using co-Present Names and Lexical ChainsJeremy Ellman, Gary Emery
  90. UNT-Yahoo: SuperSenseLearner: Combining SenseLearner with SuperSense and other Coarse Semantic FeaturesRada Mihalcea, Andras Csomai, Massimiliano Ciaramita
  91. UNT: SubFinder: Combining Knowledge Sources for Automatic Lexical SubstitutionSamer Hassan, Andras Csomai, Carmen Banea et al.
  92. UOY: A Hypergraph Model For Word Sense Induction & DisambiguationIoannis Klapaftis, Suresh Manandhar
  93. UP13: Knowledge-poor Methods (Sometimes) Perform PoorlyThierry Poibeau
  94. UPAR7: A knowledge-based system for headline sentiment taggingFrançois-Régis Chaumartin
  95. UPC: Experiments with Joint Learning within SemEval Task 9Lluís Màrquez, Lluís Padró, Mihai Surdeanu et al.
  96. UPV-SI: Word Sense Induction using Self Term ExpansionDavid Pinto, Paolo Rosso, Héctor Jiménez-Salazar
  97. UPV-WSD : Combining different WSD Methods by means of Fuzzy Borda VotingDavide Buscaldi, Paolo Rosso
  98. USFD: Preliminary Exploration of Features and Classifiers for the TempEval-2007 TaskMark Hepple, Andrea Setzer, Robert Gaizauskas
  99. USP-IBM-1 and USP-IBM-2: The ILP-based Systems for Lexical Sample WSD in SemEval-2007Lucia Specia, Maria das Graças, Volpe Nunes et al.
  100. USYD: WSD and Lexical Substitution using the Web1T corpusTobias Hawker

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