Abstract is missing.
- Front Matter [doi]
- SemEval-2013 Task 1: TempEval-3: Evaluating Time Expressions, Events, and Temporal RelationsNaushad UzZaman, Hector Llorens, Leon Derczynski, James F. Allen, Marc Verhagen, James Pustejovsky. 1-9 [doi]
- ClearTK-TimeML: A minimalist approach to TempEval 2013Steven Bethard. 10-14 [doi]
- HeidelTime: Tuning English and Developing Spanish Resources for TempEval-3Jannik Strötgen, Julian Zell, Michael Gertz. 15-19 [doi]
- ATT1: Temporal Annotation Using Big Windows and Rich Syntactic and Semantic FeaturesHyuckchul Jung, Amanda Stent. 20-24 [doi]
- Semeval-2013 Task 8: Cross-lingual Textual Entailment for Content SynchronizationMatteo Negri, Alessandro Marchetti, Yashar Mehdad, Luisa Bentivogli, Danilo Giampiccolo. 25-33 [doi]
- SOFTCARDINALITY: Learning to Identify Directional Cross-Lingual Entailment from Cardinalities and SMTSergio Jiménez 0001, Claudia Jeanneth Becerra, Alexander F. Gelbukh. 34-38 [doi]
- SemEval-2013 Task 5: Evaluating Phrasal SemanticsIoannis Korkontzelos, Torsten Zesch, Fabio Massimo Zanzotto, Chris Biemann. 39-47 [doi]
- HsH: Estimating Semantic Similarity of Words and Short Phrases with Frequency Normalized Distance MeasuresChristian Wartena. 48-52 [doi]
- ManTIME: Temporal expression identification and normalization in the TempEval-3 challengeMichele Filannino, Gavin Brown 0001, Goran Nenadic. 53-57 [doi]
- FSS-TimEx for TempEval-3: Extracting Temporal Information from TextVanni Zavarella, Hristo Tanev. 58-63 [doi]
- JU_CSE: A CRF Based Approach to Annotation of Temporal Expression, Event and Temporal RelationsAnup Kumar Kolya, Amitava Kundu, Rajdeep Gupta, Asif Ekbal, Sivaji Bandyopadhyay. 64-72 [doi]
- NavyTime: Event and Time Ordering from Raw TextNate Chambers. 73-77 [doi]
- SUTime: Evaluation in TempEval-3Angel X. Chang, Christopher D. Manning. 78-82 [doi]
- KUL: Data-driven Approach to Temporal Parsing of Newswire ArticlesOleksandr Kolomiyets, Marie-Francine Moens. 83-87 [doi]
- UTTime: Temporal Relation Classification using Deep Syntactic FeaturesNatsuda Laokulrat, Makoto Miwa, Yoshimasa Tsuruoka, Takashi Chikayama. 88-92 [doi]
- UMCC_DLSI-(EPS): Paraphrases Detection Based on Semantic DistanceHéctor Dávila, Antonio Fernández Orquín, Alexander Chavez, Yoan Gutiérrez, Armando Collazo, José Ignacio Abreu, Andrés Montoyo, Rafael Muñoz. 93-97 [doi]
- MELODI: Semantic Similarity of Words and Compositional Phrases using Latent Vector WeightingTim Van de Cruys, Stergos D. Afantenos, Philippe Muller. 98-102 [doi]
- IIRG: A Naive Approach to Evaluating Phrasal SemanticsLorna Byrne, Caroline Fenlon, John Dunnion. 103-107 [doi]
- ClaC: Semantic Relatedness of Words and PhrasesReda Siblini, Leila Kosseim. 108-113 [doi]
- UNAL: Discriminating between Literal and Figurative Phrasal Usage Using Distributional Statistics and POS tagsSergio Jiménez 0001, Claudia Jeanneth Becerra, Alexander F. Gelbukh. 114-117 [doi]
- ECNUCS: Recognizing Cross-lingual Textual Entailment Using Multiple Text Similarity and Text Difference MeasuresJiang Zhao, Man Lan, Zheng-Yu Niu. 118-123 [doi]
- BUAP: N-gram based Feature Evaluation for the Cross-Lingual Textual Entailment TaskDarnes Vilariño, David Pinto, Saúl León, Yuridiana Alemán, Helena Gómez-Adorno. 124-127 [doi]
- ALTN: Word Alignment Features for Cross-lingual Textual EntailmentMarco Turchi, Matteo Negri. 128-132 [doi]
- Umelb: Cross-lingual Textual Entailment with Word Alignment and String Similarity FeaturesYvette Graham, Bahar Salehi, Timothy Baldwin. 133-137 [doi]
- SemEval-2013 Task 4: Free Paraphrases of Noun CompoundsIris Hendrickx, Zornitsa Kozareva, Preslav Nakov, Diarmuid Ó Séaghdha, Stan Szpakowicz, Tony Veale. 138-143 [doi]
- MELODI: A Supervised Distributional Approach for Free Paraphrasing of Noun CompoundsTim Van de Cruys, Stergos D. Afantenos, Philippe Muller. 144-147 [doi]
- SFS-TUE: Compound Paraphrasing with a Language Model and Discriminative RerankingYannick Versley. 148-152 [doi]
- IIIT-H: A Corpus-Driven Co-occurrence Based Probabilistic Model for Noun Compound ParaphrasingNitesh Surtani, Arpita Batra, Urmi Ghosh, Soma Paul. 153-157 [doi]
- SemEval-2013 Task 10: Cross-lingual Word Sense DisambiguationEls Lefever, Véronique Hoste. 158-166 [doi]
- XLING: Matching Query Sentences to a Parallel Corpus using Topic Models for WSDLiling Tan, Francis Bond. 167-170 [doi]
- HLTDI: CL-WSD Using Markov Random Fields for SemEval-2013 Task 10Alex Rudnick, Can Liu, Michael Gasser. 171-177 [doi]
- LIMSI : Cross-lingual Word Sense Disambiguation using Translation Sense ClusteringMarianna Apidianaki. 178-182 [doi]
- WSD2: Parameter optimisation for Memory-based Cross-Lingual Word-Sense DisambiguationMaarten van Gompel, Antal van den Bosch. 183-187 [doi]
- NRC: A Machine Translation Approach to Cross-Lingual Word Sense Disambiguation (SemEval-2013 Task 10)Marine Carpuat. 188-192 [doi]
- SemEval-2013 Task 11: Word Sense Induction and Disambiguation within an End-User ApplicationRoberto Navigli, Daniele Vannella. 193-201 [doi]
- Duluth : Word Sense Induction Applied to Web Page ClusteringTed Pedersen. 202-206 [doi]
- SATTY : Word Sense Induction Application in Web Search ClusteringSatyabrata Behera, Upasana Gaikwad, Ramakrishna Bairi, Ganesh Ramakrishnan. 207-211 [doi]
- UKP-WSI: UKP Lab Semeval-2013 Task 11 System DescriptionHans-Peter Zorn, Iryna Gurevych. 212-216 [doi]
- unimelb: Topic Modelling-based Word Sense Induction for Web Snippet ClusteringJey Han Lau, Paul Cook, Timothy Baldwin. 217-221 [doi]
- SemEval-2013 Task 12: Multilingual Word Sense DisambiguationRoberto Navigli, David Jurgens, Daniele Vannella. 222-231 [doi]
- GETALP System : Propagation of a Lesk Measure through an Ant Colony AlgorithmDidier Schwab, Andon Tchechmedjiev, Jérôme Goulian, Mohammad Nasiruddin, Gilles Sérasset, Hervé Blanchon. 232-240 [doi]
- UMCC_DLSI: Reinforcing a Ranking Algorithm with Sense Frequencies and Multidimensional Semantic Resources to solve Multilingual Word Sense DisambiguationYoan Gutiérrez, Yenier Castañeda, Andy González, Rainel Estrada, Dennys D. Puig, José Ignacio Abreu, Roger Pérez, Antonio Fernández Orquín, Andrés Montoyo, Rafael Muñoz, Franc Camara. 241-249 [doi]
- DAEBAK!: Peripheral Diversity for Multilingual Word Sense DisambiguationSteve L. Manion, Raazesh Sainudiin. 250-254 [doi]
- SemEval-2013 Task 3: Spatial Role LabelingOleksandr Kolomiyets, Parisa KordJamshidi, Marie-Francine Moens, Steven Bethard. 255-262 [doi]
- SemEval-2013 Task 7: The Joint Student Response Analysis and 8th Recognizing Textual Entailment ChallengeMyroslava O. Dzikovska, Rodney D. Nielsen, Chris Brew, Claudia Leacock, Danilo Giampiccolo, Luisa Bentivogli, Peter Clark, Ido Dagan, Hoa Trang Dang. 263-274 [doi]
- ETS: Domain Adaptation and Stacking for Short Answer ScoringMichael Heilman, Nitin Madnani. 275-279 [doi]
- SOFTCARDINALITY: Hierarchical Text Overlap for Student Response AnalysisSergio Jiménez 0001, Claudia Jeanneth Becerra, Alexander F. Gelbukh. 280-284 [doi]
- UKP-BIU: Similarity and Entailment Metrics for Student Response AnalysisOmer Levy, Torsten Zesch, Ido Dagan, Iryna Gurevych. 285-289 [doi]
- SemEval-2013 Task 13: Word Sense Induction for Graded and Non-Graded SensesDavid Jurgens, Ioannis P. Klapaftis. 290-299 [doi]
- AI-KU: Using Substitute Vectors and Co-Occurrence Modeling For Word Sense Induction and DisambiguationOsman Baskaya, Enis Sert, Volkan Cirik, Deniz Yuret. 300-306 [doi]
- unimelb: Topic Modelling-based Word Sense InductionJey Han Lau, Paul Cook, Timothy Baldwin. 307-311 [doi]
- SemEval-2013 Task 2: Sentiment Analysis in TwitterPreslav Nakov, Sara Rosenthal, Zornitsa Kozareva, Veselin Stoyanov, Alan Ritter, Theresa Wilson. 312-320 [doi]
- NRC-Canada: Building the State-of-the-Art in Sentiment Analysis of TweetsSaif Mohammad, Svetlana Kiritchenko, Xiaodan Zhu. 321-327 [doi]
- GU-MLT-LT: Sentiment Analysis of Short Messages using Linguistic Features and Stochastic Gradient DescentTobias Günther, Lenz Furrer. 328-332 [doi]
- AVAYA: Sentiment Analysis on Twitter with Self-Training and Polarity Lexicon ExpansionLee Becker, George Erhart, David Skiba, Valentine Matula. 333-340 [doi]
- SemEval-2013 Task 9 : Extraction of Drug-Drug Interactions from Biomedical Texts (DDIExtraction 2013)Isabel Segura-Bedmar, Paloma Martínez, María Herrero-Zazo. 341-350 [doi]
- FBK-irst : A Multi-Phase Kernel Based Approach for Drug-Drug Interaction Detection and Classification that Exploits Linguistic InformationMd. Faisal Mahbub Chowdhury, Alberto Lavelli. 351-355 [doi]
- WBI-NER: The impact of domain-specific features on the performance of identifying and classifying mentions of drugsTim Rocktäschel, Torsten Huber, Michael Weidlich, Ulf Leser. 356-363 [doi]
- AMI&ERIC: How to Learn with Naive Bayes and Prior Knowledge: an Application to Sentiment AnalysisMohamed Dermouche, Leila Khouas, Julien Velcin, Sabine Loudcher. 364-368 [doi]
- UNITOR: Combining Syntactic and Semantic Kernels for Twitter Sentiment AnalysisGiuseppe Castellucci, Simone Filice, Danilo Croce, Roberto Basili. 369-374 [doi]
- TJP: Using Twitter to Analyze the Polarity of ContextsTawunrat Chalothorn, Jeremy Ellman. 375-379 [doi]
- uOttawa: System description for SemEval 2013 Task 2 Sentiment Analysis in TwitterHamid Poursepanj, Josh Weissbock, Diana Inkpen. 380-383 [doi]
- UT-DB: An Experimental Study on Sentiment Analysis in TwitterZhemin Zhu, Djoerd Hiemstra, Peter M. G. Apers, Andreas Wombacher. 384-389 [doi]
- USNA: A Dual-Classifier Approach to Contextual Sentiment AnalysisGanesh Harihara, Eugene Yang, Nate Chambers. 390-394 [doi]
- KLUE: Simple and robust methods for polarity classificationThomas Proisl, Paul Greiner, Stefan Evert, Besim Kabashi. 395-401 [doi]
- SINAI: Machine Learning and Emotion of the Crowd for Sentiment Analysis in MicroblogsEugenio Martínez-Cámara, Arturo Montejo Ráez, Maria Teresa Martín-Valdivia, Luis Alfonso Ureña López. 402-407 [doi]
- ECNUCS: A Surface Information Based System Description of Sentiment Analysis in Twitter in the SemEval-2013 (Task 2)Tiantian Zhu, Fangxi Zhang, Lan Man. 408-413 [doi]
- Umigon: sentiment analysis for tweets based on terms lists and heuristicsClement Levallois. 414-417 [doi]
- [LVIC-LIMSI]: Using Syntactic Features and Multi-polarity Words for Sentiment Analysis in TwitterMorgane Marchand, Alexandru-Lucian Gînsca, Romaric Besançon, Olivier Mesnard. 418-424 [doi]
- SwatCS: Combining simple classifiers with estimated accuracySam Clark, Rich Wicentwoski. 425-429 [doi]
- NTNU: Domain Semi-Independent Short Message Sentiment ClassificationØyvind Selmer, Mikael Brevik, Björn Gambäck, Lars Bungum. 430-437 [doi]
- SAIL: A hybrid approach to sentiment analysisNikolaos Malandrakis, Abe Kazemzadeh, Alexandros Potamianos, Shrikanth Narayanan. 438-442 [doi]
- UMCC_DLSI-(SA): Using a ranking algorithm and informal features to solve Sentiment Analysis in TwitterYoan Gutiérrez, Andy González, Roger Pérez, José Ignacio Abreu, Antonio Fernández Orquín, Alejandro Mosquera, Andrés Montoyo, Rafael Muñoz, Franc Camara. 443-449 [doi]
- ASVUniOfLeipzig: Sentiment Analysis in Twitter using Data-driven Machine Learning TechniquesRobert Remus. 450-454 [doi]
- Experiments with DBpedia, WordNet and SentiWordNet as resources for sentiment analysis in micro-bloggingHussam Hamdan, Frédéric Béchet, Patrice Bellot. 455-459 [doi]
- OPTWIMA: Comparing Knowledge-rich and Knowledge-poor Approaches for Sentiment Analysis in Short Informal TextsAlexandra Balahur. 460-465 [doi]
- FBK: Sentiment Analysis in Twitter with TweetstedMd. Faisal Mahbub Chowdhury, Marco Guerini, Sara Tonelli, Alberto Lavelli. 466-470 [doi]
- SU-Sentilab : A Classification System for Sentiment Analysis in TwitterGizem Gezici, Rahim Dehkharghani, Berrin A. Yanikoglu, Dilek Tapucu, Yücel Saygin. 471-477 [doi]
- Columbia NLP: Sentiment Detection of Subjective Phrases in Social MediaSara Rosenthal, Kathy McKeown. 478-482 [doi]
- FBM: Combining lexicon-based ML and heuristics for Social Media PolaritiesCarlos Rodríguez Penagos, Jordi Atserias Batalla, Joan Codina-Filbà, David García Narbona, Jens Grivolla, Patrik Lambert, Roser Saurí. 483-489 [doi]
- REACTION: A naive machine learning approach for sentiment classificationSilvio Moreira, João Filgueiras, Bruno Martins, Francisco M. Couto, Mário J. Silva. 490-494 [doi]
- IITB-Sentiment-Analysts: Participation in Sentiment Analysis in Twitter SemEval 2013 TaskKaran Chawla, Ankit Ramteke, Pushpak Bhattacharyya. 495-500 [doi]
- SSA-UO: Unsupervised Sentiment Analysis in TwitterReynier Ortega Bueno, Adrian Fonseca-Bruzón, Yoan Gutiérrez, Andrés Montoyo. 501-507 [doi]
- senti.ue-en: an approach for informally written short texts in SemEval-2013 Sentiment Analysis taskJosé Saias, Hilário Fernandes. 508-512 [doi]
- teragram: Rule-based detection of sentiment phrases using SAS Sentiment AnalysisHilke Reckman, Cheyanne Baird, Jean Crawford, Richard Crowell, Linnea Micciulla, Saratendu Sethi, Fruzsina Veress. 513-519 [doi]
- CodeX: Combining an SVM Classifier and Character N-gram Language Models for Sentiment Analysis on Twitter TextQi Han, Junfei Guo, Hinrich Schuetze. 520-524 [doi]
- sielers : Feature Analysis and Polarity Classification of Expressions from Twitter and SMS DataHarshit Jain, Aditya Mogadala, Vasudeva Varma. 525-529 [doi]
- Kea: Expression-level Sentiment Analysis from Twitter DataAmeeta Agrawal, Aijun An. 530-534 [doi]
- UoM: Using Explicit Semantic Analysis for Classifying SentimentsSapna Negi, Michael Rosner. 535-538 [doi]
- bwbaugh : Hierarchical sentiment analysis with partial self-trainingWesley Baugh. 539-542 [doi]
- Serendio: Simple and Practical lexicon based approach to Sentiment AnalysisPrabu palanisamy, Vineet Yadav, Harsha Elchuri. 543-548 [doi]
- SZTE-NLP: Sentiment Detection on Twitter MessagesViktor Hangya, Gábor Berend, Richárd Farkas. 549-553 [doi]
- BOUNCE: Sentiment Classification in Twitter using Rich Feature SetsNadin Kökciyan, Arda Çelebi, Arzucan Özgür, Suzan Üsküdarli. 554-561 [doi]
- nlp.cs.aueb.gr: Two Stage Sentiment AnalysisProdromos Malakasiotis, Rafael-Michael Karampatsis, Konstantina Makrynioti, John Pavlopoulos. 562-567 [doi]
- NILC_USP: A Hybrid System for Sentiment Analysis in Twitter MessagesPedro Balage Filho, Thiago Pardo. 568-572 [doi]
- UNITOR-HMM-TK: Structured Kernel-based learning for Spatial Role LabelingEmanuele Bastianelli, Danilo Croce, Roberto Basili, Daniele Nardi. 573-579 [doi]
- EHU-ALM: Similarity-Feature Based Approach for Student Response AnalysisItziar Aldabe, Montse Maritxalar, Oier Lopez de Lacalle. 580-584 [doi]
- CNGL: Grading Student Answers by Acts of TranslationErgun Biçici, Josef van Genabith. 585-591 [doi]
- Celi: EDITS and Generic Text Pair ClassificationMilen Kouylekov, Luca Dini, Alessio Bosca, Marco Trevisan. 592-597 [doi]
- LIMSIILES: Basic English Substitution for Student Answer Assessment at SemEval 2013Martin Gleize, Brigitte Grau. 598-602 [doi]
- CU : Computational Assessment of Short Free Text Answers - A Tool for Evaluating Students' UnderstandingIfeyinwa Okoye, Steven Bethard, Tamara Sumner. 603-607 [doi]
- CoMeT: Integrating different levels of linguistic modeling for meaning assessmentNiels Ott, Ramon Ziai, Michael Hahn 0001, Detmar Meurers. 608-616 [doi]
- UC3M: A kernel-based approach to identify and classify DDIs in bio-medical textsDaniel Sánchez-Cisneros. 617-621 [doi]
- UEM-UC3M: An Ontology-based named entity recognition system for biomedical textsDaniel Sánchez-Cisneros, Fernando Aparicio Gali. 622-627 [doi]
- WBI-DDI: Drug-Drug Interaction Extraction using Majority VotingPhilippe E. Thomas, Mariana L. Neves, Tim Rocktäschel, Ulf Leser. 628-635 [doi]
- UMCC_DLSI: Semantic and Lexical features for detection and classification Drugs in biomedical textsArmando Collazo, Alberto Ceballo, Dennys D. Puig, Yoan Gutiérrez, José Ignacio Abreu, Roger Pérez, Antonio Fernández Orquín, Andrés Montoyo, Rafael Muñoz, Franc Camara. 636-643 [doi]
- NIL_UCM: Extracting Drug-Drug interactions from text through combination of sequence and tree kernelsBehrouz Bokharaeian, Alberto Díaz 0001. 644-650 [doi]
- UTurku: Drug Named Entity Recognition and Drug-Drug Interaction Extraction Using SVM Classification and Domain KnowledgeJari Björne, Suwisa Kaewphan, Tapio Salakoski. 651-659 [doi]
- LASIGE: using Conditional Random Fields and ChEBI ontologyTiago Grego, Francisco R. Pinto, Francisco M. Couto. 660-666 [doi]
- UWM-TRIADS: Classifying Drug-Drug Interactions with Two-Stage SVM and Post-ProcessingMajid Rastegar-Mojarad, Richard D. Boyce, Rashmi Prasad. 667-674 [doi]
- SCAI: Extracting drug-drug interactions using a rich feature vectorTamara Bobic, Juliane Fluck, Martin Hofmann-Apitius. 675-683 [doi]
- UColorado_SOM: Extraction of Drug-Drug Interactions from Biomedical Text using Knowledge-rich and Knowledge-poor FeaturesNegacy D. Hailu, Lawrence E. Hunter, K. Bretonnel Cohen. 684-688 [doi]
- UoS: A Graph-Based System for Graded Word Sense InductionDavid Hope, Bill Keller. 689-694 [doi]