Table of Contents

• Front Matter [doi]
• SemEval-2010 Task 1: Coreference Resolution in Multiple LanguagesMarta Recasens, Lluís Màrquez, Emili Sapena, M. Antònia Martí, Mariona Taulé, Véronique Hoste, Massimo Poesio, Yannick Versley. 1-8 [doi]
• SemEval-2010 Task 2: Cross-Lingual Lexical SubstitutionRada Mihalcea, Ravi Som Sinha, Diana McCarthy. 9-14 [doi]
• SemEval-2010 Task 3: Cross-Lingual Word Sense DisambiguationEls Lefever, Véronique Hoste. 15-20 [doi]
• SemEval-2010 Task 5 : Automatic Keyphrase Extraction from Scientific ArticlesSu Nam Kim, Olena Medelyan, Min-Yen Kan, Timothy Baldwin. 21-26 [doi]
• SemEval-2010 Task 7: Argument Selection and CoercionJames Pustejovsky, Anna Rumshisky, Alex Plotnick, Elisabetta Jezek, Olga Batiukova, Valeria Quochi. 27-32 [doi]
• SemEval-2010 Task 8: Multi-Way Classification of Semantic Relations between Pairs of NominalsIris Hendrickx, Su Nam Kim, Zornitsa Kozareva, Preslav Nakov, Diarmuid Ó Séaghdha, Sebastian Padó, Marco Pennacchiotti, Lorenza Romano, Stan Szpakowicz. 33-38 [doi]
• SemEval-2 Task 9: The Interpretation of Noun Compounds Using Paraphrasing Verbs and PrepositionsCristina Butnariu, Su Nam Kim, Preslav Nakov, Diarmuid Ó Séaghdha, Stan Szpakowicz, Tony Veale. 39-44 [doi]
• SemEval-2010 Task 10: Linking Events and Their Participants in DiscourseJosef Ruppenhofer, Caroline Sporleder, Roser Morante, Collin Baker, Martha Palmer. 45-50 [doi]
• SemEval-2010 Task 12: Parser Evaluation Using Textual EntailmentsDeniz Yuret, Aydin Han, Zehra Turgut. 51-56 [doi]
• SemEval-2010 Task 13: TempEval-2Marc Verhagen, Roser Saurí, Tommaso Caselli, James Pustejovsky. 57-62 [doi]
• SemEval-2010 Task 14: Word Sense Induction &DisambiguationSuresh Manandhar, Ioannis P. Klapaftis, Dmitriy Dligach, Sameer Pradhan. 63-68 [doi]
• SemEval-2010 Task: Japanese WSDManabu Okumura, Kiyoaki Shirai, Kanako Komiya, Hikaru Yokono. 69-74 [doi]
• SemEval-2010 Task 17: All-Words Word Sense Disambiguation on a Specific DomainEneko Agirre, Oier Lopez de Lacalle, Christiane Fellbaum, Shu-Kai Hsieh, Maurizio Tesconi, Monica Monachini, Piek Vossen, Roxanne Segers. 75-80 [doi]
• SemEval-2010 Task 18: Disambiguating Sentiment Ambiguous AdjectivesYunfang Wu, Peng Jin. 81-85 [doi]
• SemEval-2010 Task 11: Event Detection in Chinese News SentencesQiang Zhou. 86 [doi]
• SemEval-2 Task 15: Infrequent Sense Identification for Mandarin Text to Speech SystemsPeng Jin, Yunfang Wu. 87 [doi]
• RelaxCor: A Global Relaxation Labeling Approach to Coreference ResolutionEmili Sapena, Lluís Padró, Jordi Turmo. 88-91 [doi]
• SUCRE: A Modular System for Coreference ResolutionHamidreza Kobdani, Hinrich Schütze. 92-95 [doi]
• UBIU: A Language-Independent System for Coreference ResolutionDesislava Zhekova, Sandra Kübler. 96-99 [doi]
• Corry: A System for Coreference ResolutionOlga Uryupina. 100-103 [doi]
• BART: A Multilingual Anaphora Resolution SystemSamuel Broscheit, Massimo Poesio, Simone Paolo Ponzetto, Kepa Joseba Rodríguez, Lorenza Romano, Olga Uryupina, Yannick Versley, Roberto Zanoli. 104-107 [doi]
• TANL-1: Coreference Resolution by Parse Analysis and Similarity ClusteringGiuseppe Attardi, Maria Simi, Stefano Dei Rossi. 108-111 [doi]
• FCC: Modeling Probabilities with GIZA++ for Task 2 and 3 of SemEval-2Darnes Vilariño Ayala, Carlos Balderas Posada, David Eduardo Pinto Avendaño, Miguel Rodríguez Hernández, Saúl León Silverio. 112-116 [doi]
• Combining Dictionaries and Contextual Information for Cross-Lingual Lexical SubstitutionWilker Aziz, Lucia Specia. 117-122 [doi]
• SWAT: Cross-Lingual Lexical Substitution using Local Context Matching, Bilingual Dictionaries and Machine TranslationRichard Wicentowski, Maria Kelly, Rachel Lee. 123-128 [doi]
• COLEPL and COLSLM: An Unsupervised WSD Approach to Multilingual Lexical Substitution, Tasks 2 and 3 SemEval 2010Weiwei Guo, Mona T. Diab. 129-133 [doi]
• UHD: Cross-Lingual Word Sense Disambiguation Using Multilingual Co-Occurrence GraphsCarina Silberer, Simone Paolo Ponzetto. 134-137 [doi]
• OWNS: Cross-lingual Word Sense Disambiguation Using Weighted Overlap Counts and Wordnet Based Similarity MeasuresLipta Mahapatra, Meera Mohan, Mitesh M. Khapra, Pushpak Bhattacharyya. 138-141 [doi]
• 273. Task 5. Keyphrase Extraction Based on Core Word Identification and Word ExpansionOuyang You, Wenjie Li, Renxian Zhang. 142-145 [doi]
• DERIUNLP: A Context Based Approach to Automatic Keyphrase ExtractionGeorgeta Bordea, Paul Buitelaar. 146-149 [doi]
• DFKI KeyWE: Ranking Keyphrases Extracted from Scientific ArticlesKathrin Eichler, Günter Neumann. 150-153 [doi]
• Single Document Keyphrase Extraction Using Sentence Clustering and Latent Dirichlet AllocationClaude Pasquier. 154-157 [doi]
• SJTULTLAB: Chunk Based Method for Keyphrase ExtractionLetian Wang, Fang Li. 158-161 [doi]
• Likey: Unsupervised Language-Independent Keyphrase ExtractionMari-Sanna Paukkeri, Timo Honkela. 162-165 [doi]
• WINGNUS: Keyphrase Extraction Utilizing Document Logical StructureThuy Dung Nguyen, Minh-Thang Luong. 166-169 [doi]
• KX: A Flexible System for Keyphrase eXtractionEmanuele Pianta, Sara Tonelli. 170-173 [doi]
• BUAP: An Unsupervised Approach to Automatic Keyphrase Extraction from Scientific ArticlesRoberto Ortiz, David Pinto, Mireya Tovar, Héctor Jiménez-Salazar. 174-177 [doi]
• UNPMC: Naive Approach to Extract Keyphrases from Scientific ArticlesJungyeul Park, Jong Gun Lee, Béatrice Daille. 178-181 [doi]
• SEERLAB: A System for Extracting Keyphrases from Scholarly DocumentsPucktada Treeratpituk, Pradeep B. Teregowda, Jian Huang 0002, C. Lee Giles. 182-185 [doi]
• SZTERGAK : Feature Engineering for Keyphrase ExtractionGábor Berend, Richárd Farkas. 186-189 [doi]
• KP-Miner: Participation in SemEval-2Samhaa R. El-Beltagy, Ahmed A. Rafea. 190-193 [doi]
• UvT: The UvT Term Extraction System in the Keyphrase Extraction TaskKalliopi Zervanou. 194-197 [doi]
• UNITN: Part-Of-Speech Counting in Relation ExtractionFabio Celli. 198-201 [doi]
• FBK_NK: A WordNet-Based System for Multi-Way Classification of Semantic RelationsMatteo Negri, Milen Kouylekov. 202-205 [doi]
• JU: A Supervised Approach to Identify Semantic Relations from Paired NominalsSantanu Pal, Partha Pakray, Dipankar Das, Sivaji Bandyopadhyay. 206-209 [doi]
• TUD: Semantic Relatedness for Relation ClassificationGyörgy Szarvas, Iryna Gurevych. 210-213 [doi]
• FBK-IRST: Semantic Relation Extraction Using CycKateryna Tymoshenko, Claudio Giuliano. 214-217 [doi]
• ISTI@SemEval-2 Task 8: Boosting-Based Multiway Relation ClassificationAndrea Esuli, Diego Marcheggiani, Fabrizio Sebastiani 0001. 218-221 [doi]
• ISI: Automatic Classification of Relations Between Nominals Using a Maximum Entropy ClassifierStephen Tratz, Eduard H. Hovy. 222-225 [doi]
• ECNU: Effective Semantic Relations Classification without Complicated Features or Multiple External CorporaYuan Chen, Man Lan, Jian Su, Zhi-Min Zhou, Yu Xu. 226-229 [doi]
• UCD-Goggle: A Hybrid System for Noun Compound ParaphrasingGuofu Li, Alejandra Lopez-Fernandez, Tony Veale. 230-233 [doi]
• UCD-PN: Selecting General Paraphrases Using Conditional ProbabilityPaul Nulty, Fintan Costello. 234-237 [doi]
• UvT-WSD1: A Cross-Lingual Word Sense Disambiguation SystemMaarten van Gompel. 238-241 [doi]
• UBA: Using Automatic Translation and Wikipedia for Cross-Lingual Lexical SubstitutionPierpaolo Basile, Giovanni Semeraro. 242-247 [doi]
• HUMB: Automatic Key Term Extraction from Scientific Articles in GROBIDPatrice Lopez, Laurent Romary. 248-251 [doi]
• UTDMet: Combining WordNet and Corpus Data for Argument Coercion DetectionKirk Roberts, Sanda M. Harabagiu. 252-255 [doi]
• UTD: Classifying Semantic Relations by Combining Lexical and Semantic ResourcesBryan Rink, Sanda M. Harabagiu. 256-259 [doi]
• UvT: Memory-Based Pairwise Ranking of Paraphrasing VerbsSander Wubben. 260-263 [doi]
• SEMAFOR: Frame Argument Resolution with Log-Linear ModelsDesai Chen, Nathan Schneider, Dipanjan Das 0001, Noah A. Smith. 264-267 [doi]
• Cambridge: Parser Evaluation Using Textual Entailment by Grammatical Relation ComparisonLaura Rimell, Stephen Clark. 268-271 [doi]
• MARS: A Specialized RTE System for Parser EvaluationRui Wang, Yi Zhang. 272-275 [doi]
• TRIPS and TRIOS System for TempEval-2: Extracting Temporal Information from TextNaushad UzZaman, James F. Allen. 276-283 [doi]
• TIPSem (English and Spanish): Evaluating CRFs and Semantic Roles in TempEval-2Hector Llorens, Estela Saquete, Borja Navarro. 284-291 [doi]
• CityU-DAC: Disambiguating Sentiment-Ambiguous Adjectives within ContextBin Lu, Benjamin Ka-Yin T'sou. 292-295 [doi]
• VENSES++: Adapting a deep semantic processing system to the identification of null instantiationsSara Tonelli, Rodolfo Delmonte. 296-299 [doi]
• CLR: Linking Events and Their Participants in Discourse Using a Comprehensive FrameNet DictionaryKen Litkowski. 300-303 [doi]
• PKU_HIT: An Event Detection System Based on Instances Expansion and Rich Syntactic FeaturesShiqi Li, Pengyuan Liu, Tiejun Zhao, Qin Lu, Hanjing Li. 304-307 [doi]
• 372: Comparing the Benefit of Different Dependency Parsers for Textual Entailment Using Syntactic Constraints OnlyAlexander Volokh, Günter Neumann. 308-312 [doi]
• SCHWA: PETE Using CCG Dependencies with the C&C ParserDominick Ng, James W. D. Constable, Matthew Honnibal, James R. Curran. 313-316 [doi]
• ID 392: TERSEO + T2T3 Transducer. A systems for Recognizing and Normalizing TIMEX3Estela Saquete Boro. 317-320 [doi]
• HeidelTime: High Quality Rule-Based Extraction and Normalization of Temporal ExpressionsJannik Strötgen, Michael Gertz. 321-324 [doi]
• KUL: Recognition and Normalization of Temporal ExpressionsOleksandr Kolomiyets, Marie-Francine Moens. 325-328 [doi]
• UC3M System: Determining the Extent, Type and Value of Time Expressions in TempEval-2Maria Teresa Vicente-Díez, Julián Moreno Schneider, Paloma Martínez. 329-332 [doi]
• Edinburgh-LTG: TempEval-2 System DescriptionClaire Grover, Richard Tobin, Beatrice Alex, Kate Byrne. 333-336 [doi]
• USFD2: Annotating Temporal Expresions and TLINKs for TempEval-2Leon Derczynski, Robert J. Gaizauskas. 337-340 [doi]
• NCSU: Modeling Temporal Relations with Markov Logic and Lexical OntologyEun Ha, Alok Baikadi, Carlyle Licata, James C. Lester. 341-344 [doi]
• JU_CSE_TEMP: A First Step towards Evaluating Events, Time Expressions and Temporal RelationsAnup Kumar Kolya, Asif Ekbal, Sivaji Bandyopadhyay. 345-350 [doi]
• KCDC: Word Sense Induction by Using Grammatical Dependencies and Sentence Phrase StructureRoman Kern, Markus Muhr, Michael Granitzer. 351-354 [doi]
• UoY: Graphs of Unambiguous Vertices for Word Sense Induction and DisambiguationIoannis Korkontzelos, Suresh Manandhar. 355-358 [doi]
• HERMIT: Flexible Clustering for the SemEval-2 WSI TaskDavid Jurgens, Keith Stevens. 359-362 [doi]
• Duluth-WSI: SenseClusters Applied to the Sense Induction Task of SemEval-2Ted Pedersen. 363-366 [doi]
• KSU KDD: Word Sense Induction by Clustering in Topic SpaceWesam Elshamy, Doina Caragea, William H. Hsu. 367-370 [doi]
• PengYuan@PKU: Extracting Infrequent Sense Instance with the Same N-Gram Pattern for the SemEval-2010 Task 15Pengyuan Liu, Shiwen Yu, Shui Liu, Tiejun Zhao. 371-374 [doi]
• RALI: Automatic Weighting of Text Window DistancesBernard Brosseau-Villeneuve, Noriko Kando, Jian-Yun Nie. 375-378 [doi]
• JAIST: Clustering and Classification Based Approaches for Japanese WSDKiyoaki Shirai, Makoto Nakamura. 379-382 [doi]
• MSS: Investigating the Effectiveness of Domain Combinations and Topic Features for Word Sense DisambiguationSanae Fujita, Kevin Duh, Akinori Fujino, Hirotoshi Taira, Hiroyuki Shindo. 383-386 [doi]
• IIITH: Domain Specific Word Sense DisambiguationSiva Reddy, Abhilash Inumella, Diana McCarthy, Mark Stevenson. 387-391 [doi]
• UCF-WS: Domain Word Sense Disambiguation Using Web SelectorsHansen Andrew Schwartz, Fernando Gomez. 392-395 [doi]
• TreeMatch: A Fully Unsupervised WSD System Using Dependency Knowledge on a Specific DomainAndrew Tran, Chris Bowes, David Brown, Ping Chen, Max Choly, Wei Ding 0003. 396-401 [doi]
• GPLSI-IXA: Using Semantic Classes to Acquire Monosemous Training Examples from Domain TextsRubén Izquierdo, Armando Suárez, German Rigau. 402-406 [doi]
• HIT-CIR: An Unsupervised WSD System Based on Domain Most Frequent Sense EstimationYuhang Guo, Wanxiang Che, Wei He, Ting Liu, Sheng Li. 407-410 [doi]
• RACAI: Unsupervised WSD Experiments @ SemEval-2, Task 17Radu Ion, Dan Stefanescu. 411-416 [doi]
• Kyoto: An Integrated System for Specific Domain WSDAitor Soroa, Eneko Agirre, Oier Lopez de Lacalle, Wauter Bosma, Piek Vossen, Monica Monachini, Jessie Lo, Shu-Kai Hsieh. 417-420 [doi]
• CFILT: Resource Conscious Approaches for All-Words Domain Specific WSDAnup Kulkarni, Mitesh M. Khapra, Saurabh Sohoney, Pushpak Bhattacharyya. 421-426 [doi]
• UMCC-DLSI: Integrative Resource for Disambiguation TaskYoan Gutiérrez-Vázquez, Antonio Fernández Orquín, Andrés Montoyo-Guijarro, Sonia Vázquez Pérez. 427-432 [doi]
• HR-WSD: System Description for All-Words Word Sense Disambiguation on a Specific Domain at SemEval-2010Meng-Hsien Shih. 433-435 [doi]
• Twitter Based System: Using Twitter for Disambiguating Sentiment Ambiguous AdjectivesAlexander Pak, Patrick Paroubek. 436-439 [doi]
• YSC-DSAA: An Approach to Disambiguate Sentiment Ambiguous Adjectives Based on SAAOLShi-Cai Yang, Mei-Juan Liu. 440-443 [doi]
• OpAL: Applying Opinion Mining Techniques for the Disambiguation of Sentiment Ambiguous Adjectives in SemEval-2 Task 18Alexandra Balahur, Andrés Montoyo. 444-447 [doi]
• HITSZ_CITYU: Combine Collocation, Context Words and Neighboring Sentence Sentiment in Sentiment Adjectives DisambiguationRuifeng Xu, Jun Xu 0007, Chunyu Kit. 448-451 [doi]