default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDSemEval@NAACL-HLT 2013: Atlanta, Georgia, USA
- Mona T. Diab, Timothy Baldwin, Marco Baroni:
Proceedings of the 7th International Workshop on Semantic Evaluation, SemEval@NAACL-HLT 2013, Atlanta, Georgia, USA, June 14-15, 2013. The Association for Computer Linguistics 2013, ISBN 978-1-937284-49-7 - Naushad UzZaman, Hector Llorens, Leon Derczynski, James F. Allen, Marc Verhagen, James Pustejovsky:
SemEval-2013 Task 1: TempEval-3: Evaluating Time Expressions, Events, and Temporal Relations. 1-9 - Steven Bethard:
ClearTK-TimeML: A minimalist approach to TempEval 2013. 10-14 - Jannik Strötgen, Julian Zell, Michael Gertz:
HeidelTime: Tuning English and Developing Spanish Resources for TempEval-3. 15-19 - Hyuckchul Jung, Amanda Stent:
ATT1: Temporal Annotation Using Big Windows and Rich Syntactic and Semantic Features. 20-24 - Matteo Negri, Alessandro Marchetti, Yashar Mehdad, Luisa Bentivogli, Danilo Giampiccolo:
Semeval-2013 Task 8: Cross-lingual Textual Entailment for Content Synchronization. 25-33 - Sergio Jiménez, Claudia Jeanneth Becerra, Alexander F. Gelbukh:
SOFTCARDINALITY: Learning to Identify Directional Cross-Lingual Entailment from Cardinalities and SMT. 34-38 - Ioannis Korkontzelos, Torsten Zesch, Fabio Massimo Zanzotto, Chris Biemann:
SemEval-2013 Task 5: Evaluating Phrasal Semantics. 39-47 - Christian Wartena:
HsH: Estimating Semantic Similarity of Words and Short Phrases with Frequency Normalized Distance Measures. 48-52 - Michele Filannino, Gavin Brown, Goran Nenadic:
ManTIME: Temporal expression identification and normalization in the TempEval-3 challenge. 53-57 - Vanni Zavarella, Hristo Tanev:
FSS-TimEx for TempEval-3: Extracting Temporal Information from Text. 58-63 - Anup Kumar Kolya, Amitava Kundu, Rajdeep Gupta, Asif Ekbal, Sivaji Bandyopadhyay:
JU_CSE: A CRF Based Approach to Annotation of Temporal Expression, Event and Temporal Relations. 64-72 - Nate Chambers:
NavyTime: Event and Time Ordering from Raw Text. 73-77 - Angel X. Chang, Christopher D. Manning:
SUTime: Evaluation in TempEval-3. 78-82 - Oleksandr Kolomiyets, Marie-Francine Moens:
KUL: Data-driven Approach to Temporal Parsing of Newswire Articles. 83-87 - Natsuda Laokulrat, Makoto Miwa, Yoshimasa Tsuruoka, Takashi Chikayama:
UTTime: Temporal Relation Classification using Deep Syntactic Features. 88-92 - Héctor Dávila, Antonio Fernández Orquín, Alexander Chavez, Yoan Gutiérrez, Armando Collazo, José Ignacio Abreu, Andrés Montoyo, Rafael Muñoz:
UMCC_DLSI-(EPS): Paraphrases Detection Based on Semantic Distance. 93-97 - Tim Van de Cruys, Stergos D. Afantenos, Philippe Muller:
MELODI: Semantic Similarity of Words and Compositional Phrases using Latent Vector Weighting. 98-102 - Lorna Byrne, Caroline Fenlon, John Dunnion:
IIRG: A Naive Approach to Evaluating Phrasal Semantics. 103-107 - Reda Siblini, Leila Kosseim:
ClaC: Semantic Relatedness of Words and Phrases. 108-113 - Sergio Jiménez, Claudia Jeanneth Becerra, Alexander F. Gelbukh:
UNAL: Discriminating between Literal and Figurative Phrasal Usage Using Distributional Statistics and POS tags. 114-117 - Jiang Zhao, Man Lan, Zheng-Yu Niu:
ECNUCS: Recognizing Cross-lingual Textual Entailment Using Multiple Text Similarity and Text Difference Measures. 118-123 - Darnes Vilariño, David Pinto, Saúl León, Yuridiana Alemán, Helena Gómez-Adorno:
BUAP: N-gram based Feature Evaluation for the Cross-Lingual Textual Entailment Task. 124-127 - Marco Turchi, Matteo Negri:
ALTN: Word Alignment Features for Cross-lingual Textual Entailment. 128-132 - Yvette Graham, Bahar Salehi, Timothy Baldwin:
Umelb: Cross-lingual Textual Entailment with Word Alignment and String Similarity Features. 133-137 - Iris Hendrickx, Zornitsa Kozareva, Preslav Nakov, Diarmuid Ó Séaghdha, Stan Szpakowicz, Tony Veale:
SemEval-2013 Task 4: Free Paraphrases of Noun Compounds. 138-143 - Tim Van de Cruys, Stergos D. Afantenos, Philippe Muller:
MELODI: A Supervised Distributional Approach for Free Paraphrasing of Noun Compounds. 144-147 - Yannick Versley:
SFS-TUE: Compound Paraphrasing with a Language Model and Discriminative Reranking. 148-152 - Nitesh Surtani, Arpita Batra, Urmi Ghosh, Soma Paul:
IIIT-H: A Corpus-Driven Co-occurrence Based Probabilistic Model for Noun Compound Paraphrasing. 153-157 - Els Lefever, Véronique Hoste:
SemEval-2013 Task 10: Cross-lingual Word Sense Disambiguation. 158-166 - Liling Tan, Francis Bond:
XLING: Matching Query Sentences to a Parallel Corpus using Topic Models for WSD. 167-170 - Alex Rudnick, Can Liu, Michael Gasser:
HLTDI: CL-WSD Using Markov Random Fields for SemEval-2013 Task 10. 171-177 - Marianna Apidianaki:
LIMSI : Cross-lingual Word Sense Disambiguation using Translation Sense Clustering. 178-182 - Maarten van Gompel, Antal van den Bosch:
WSD2: Parameter optimisation for Memory-based Cross-Lingual Word-Sense Disambiguation. 183-187 - Marine Carpuat:
NRC: A Machine Translation Approach to Cross-Lingual Word Sense Disambiguation (SemEval-2013 Task 10). 188-192 - Roberto Navigli, Daniele Vannella:
SemEval-2013 Task 11: Word Sense Induction and Disambiguation within an End-User Application. 193-201 - Ted Pedersen:
Duluth : Word Sense Induction Applied to Web Page Clustering. 202-206 - Satyabrata Behera, Upasana Gaikwad, Ramakrishna Bairi, Ganesh Ramakrishnan:
SATTY : Word Sense Induction Application in Web Search Clustering. 207-211 - Hans-Peter Zorn, Iryna Gurevych:
UKP-WSI: UKP Lab Semeval-2013 Task 11 System Description. 212-216 - Jey Han Lau, Paul Cook, Timothy Baldwin:
unimelb: Topic Modelling-based Word Sense Induction for Web Snippet Clustering. 217-221 - Roberto Navigli, David Jurgens, Daniele Vannella:
SemEval-2013 Task 12: Multilingual Word Sense Disambiguation. 222-231 - Didier Schwab, Andon Tchechmedjiev, Jérôme Goulian, Mohammad Nasiruddin, Gilles Sérasset, Hervé Blanchon:
GETALP System : Propagation of a Lesk Measure through an Ant Colony Algorithm. 232-240 - Yoan Gutiérrez, Yenier Castañeda, Andy González, Rainel Estrada, Dennys D. Puig, José Ignacio Abreu, Roger Pérez, Antonio Fernández Orquín, Andrés Montoyo, Rafael Muñoz, Franc Camara:
UMCC_DLSI: Reinforcing a Ranking Algorithm with Sense Frequencies and Multidimensional Semantic Resources to solve Multilingual Word Sense Disambiguation. 241-249 - Steve L. Manion, Raazesh Sainudiin:
DAEBAK!: Peripheral Diversity for Multilingual Word Sense Disambiguation. 250-254 - Oleksandr Kolomiyets, Parisa Kordjamshidi, Marie-Francine Moens, Steven Bethard:
SemEval-2013 Task 3: Spatial Role Labeling. 255-262 - Myroslava O. Dzikovska, Rodney D. Nielsen, Chris Brew, Claudia Leacock, Danilo Giampiccolo, Luisa Bentivogli, Peter Clark, Ido Dagan, Hoa Trang Dang:
SemEval-2013 Task 7: The Joint Student Response Analysis and 8th Recognizing Textual Entailment Challenge. 263-274 - Michael Heilman, Nitin Madnani:
ETS: Domain Adaptation and Stacking for Short Answer Scoring. 275-279 - Sergio Jiménez, Claudia Jeanneth Becerra, Alexander F. Gelbukh:
SOFTCARDINALITY: Hierarchical Text Overlap for Student Response Analysis. 280-284 - Omer Levy, Torsten Zesch, Ido Dagan, Iryna Gurevych:
UKP-BIU: Similarity and Entailment Metrics for Student Response Analysis. 285-289 - David Jurgens, Ioannis P. Klapaftis:
SemEval-2013 Task 13: Word Sense Induction for Graded and Non-Graded Senses. 290-299 - Osman Baskaya, Enis Sert, Volkan Cirik, Deniz Yuret:
AI-KU: Using Substitute Vectors and Co-Occurrence Modeling For Word Sense Induction and Disambiguation. 300-306 - Jey Han Lau, Paul Cook, Timothy Baldwin:
unimelb: Topic Modelling-based Word Sense Induction. 307-311 - Preslav Nakov, Sara Rosenthal, Zornitsa Kozareva, Veselin Stoyanov, Alan Ritter, Theresa Wilson:
SemEval-2013 Task 2: Sentiment Analysis in Twitter. 312-320 - Saif M. Mohammad, Svetlana Kiritchenko, Xiaodan Zhu:
NRC-Canada: Building the State-of-the-Art in Sentiment Analysis of Tweets. 321-327 - Tobias Günther, Lenz Furrer:
GU-MLT-LT: Sentiment Analysis of Short Messages using Linguistic Features and Stochastic Gradient Descent. 328-332 - Lee Becker, George Erhart, David Skiba, Valentine Matula:
AVAYA: Sentiment Analysis on Twitter with Self-Training and Polarity Lexicon Expansion. 333-340 - Isabel Segura-Bedmar
, Paloma Martínez, María Herrero-Zazo:
SemEval-2013 Task 9 : Extraction of Drug-Drug Interactions from Biomedical Texts (DDIExtraction 2013). 341-350 - Md. Faisal Mahbub Chowdhury, Alberto Lavelli:
FBK-irst : A Multi-Phase Kernel Based Approach for Drug-Drug Interaction Detection and Classification that Exploits Linguistic Information. 351-355 - Tim Rocktäschel, Torsten Huber, Michael Weidlich, Ulf Leser:
WBI-NER: The impact of domain-specific features on the performance of identifying and classifying mentions of drugs. 356-363 - Mohamed Dermouche, Leila Khouas, Julien Velcin, Sabine Loudcher:
AMI&ERIC: How to Learn with Naive Bayes and Prior Knowledge: an Application to Sentiment Analysis. 364-368 - Giuseppe Castellucci, Simone Filice, Danilo Croce, Roberto Basili:
UNITOR: Combining Syntactic and Semantic Kernels for Twitter Sentiment Analysis. 369-374 - Tawunrat Chalothorn, Jeremy Ellman:
TJP: Using Twitter to Analyze the Polarity of Contexts. 375-379 - Hamid Poursepanj, Josh Weissbock, Diana Inkpen:
uOttawa: System description for SemEval 2013 Task 2 Sentiment Analysis in Twitter. 380-383 - Zhemin Zhu, Djoerd Hiemstra, Peter M. G. Apers, Andreas Wombacher:
UT-DB: An Experimental Study on Sentiment Analysis in Twitter. 384-389 - Ganesh Harihara, Eugene Yang, Nate Chambers:
USNA: A Dual-Classifier Approach to Contextual Sentiment Analysis. 390-394 - Thomas Proisl, Paul Greiner, Stefan Evert, Besim Kabashi:
KLUE: Simple and robust methods for polarity classification. 395-401 - Eugenio Martínez-Cámara, Arturo Montejo-Ráez, María Teresa Martín-Valdivia, Luis Alfonso Ureña López:
SINAI: Machine Learning and Emotion of the Crowd for Sentiment Analysis in Microblogs. 402-407 - Tiantian Zhu, Fangxi Zhang, Lan Man:
ECNUCS: A Surface Information Based System Description of Sentiment Analysis in Twitter in the SemEval-2013 (Task 2). 408-413 - Clement Levallois:
Umigon: sentiment analysis for tweets based on terms lists and heuristics. 414-417 - Morgane Marchand, Alexandru-Lucian Gînsca, Romaric Besançon, Olivier Mesnard:
[LVIC-LIMSI]: Using Syntactic Features and Multi-polarity Words for Sentiment Analysis in Twitter. 418-424 - Sam Clark, Rich Wicentwoski:
SwatCS: Combining simple classifiers with estimated accuracy. 425-429 - Øyvind Selmer, Mikael Brevik, Björn Gambäck, Lars Bungum:
NTNU: Domain Semi-Independent Short Message Sentiment Classification. 430-437 - Nikolaos Malandrakis, Abe Kazemzadeh, Alexandros Potamianos, Shrikanth S. Narayanan:
SAIL: A hybrid approach to sentiment analysis. 438-442 - Yoan Gutiérrez, Andy González, Roger Pérez, José Ignacio Abreu, Antonio Fernández Orquín, Alejandro Mosquera López, Andrés Montoyo, Rafael Muñoz, Franc Camara:
UMCC_DLSI-(SA): Using a ranking algorithm and informal features to solve Sentiment Analysis in Twitter. 443-449 - Robert Remus:
ASVUniOfLeipzig: Sentiment Analysis in Twitter using Data-driven Machine Learning Techniques. 450-454 - Hussam Hamdan, Frédéric Béchet, Patrice Bellot:
Experiments with DBpedia, WordNet and SentiWordNet as resources for sentiment analysis in micro-blogging. 455-459 - Alexandra Balahur:
OPTWIMA: Comparing Knowledge-rich and Knowledge-poor Approaches for Sentiment Analysis in Short Informal Texts. 460-465 - Md. Faisal Mahbub Chowdhury, Marco Guerini, Sara Tonelli, Alberto Lavelli:
FBK: Sentiment Analysis in Twitter with Tweetsted. 466-470 - Gizem Gezici, Rahim Dehkharghani, Berrin A. Yanikoglu, Dilek Tapucu, Yücel Saygin:
SU-Sentilab : A Classification System for Sentiment Analysis in Twitter. 471-477 - Sara Rosenthal, Kathy McKeown:
Columbia NLP: Sentiment Detection of Subjective Phrases in Social Media. 478-482 - Carlos Rodríguez Penagos, Jordi Atserias Batalla, Joan Codina-Filbà, David García Narbona, Jens Grivolla, Patrik Lambert, Roser Saurí:
FBM: Combining lexicon-based ML and heuristics for Social Media Polarities. 483-489 - Silvio Moreira, João Filgueiras, Bruno Martins, Francisco M. Couto, Mário J. Silva:
REACTION: A naive machine learning approach for sentiment classification. 490-494 - Karan Chawla, Ankit Ramteke, Pushpak Bhattacharyya:
IITB-Sentiment-Analysts: Participation in Sentiment Analysis in Twitter SemEval 2013 Task. 495-500 - Reynier Ortega Bueno, Adrian Fonseca Bruzón, Yoan Gutiérrez, Andrés Montoyo:
SSA-UO: Unsupervised Sentiment Analysis in Twitter. 501-507 - José Saias, Hilário Fernandes:
senti.ue-en: an approach for informally written short texts in SemEval-2013 Sentiment Analysis task. 508-512 - Hilke Reckman, Cheyanne Baird, Jean Crawford, Richard Crowell, Linnea Micciulla, Saratendu Sethi, Fruzsina Veress:
teragram: Rule-based detection of sentiment phrases using SAS Sentiment Analysis. 513-519 - Qi Han, Junfei Guo, Hinrich Schütze:
CodeX: Combining an SVM Classifier and Character N-gram Language Models for Sentiment Analysis on Twitter Text. 520-524 - Harshit Jain, Aditya Mogadala, Vasudeva Varma:
sielers : Feature Analysis and Polarity Classification of Expressions from Twitter and SMS Data. 525-529 - Ameeta Agrawal, Aijun An:
Kea: Expression-level Sentiment Analysis from Twitter Data. 530-534 - Sapna Negi, Michael Rosner:
UoM: Using Explicit Semantic Analysis for Classifying Sentiments. 535-538 - Wesley Baugh:
bwbaugh : Hierarchical sentiment analysis with partial self-training. 539-542 - Prabu palanisamy, Vineet Yadav, Harsha Elchuri:
Serendio: Simple and Practical lexicon based approach to Sentiment Analysis. 543-548 - Viktor Hangya, Gábor Berend, Richárd Farkas:
SZTE-NLP: Sentiment Detection on Twitter Messages. 549-553 - Nadin Kökciyan, Arda Çelebi, Arzucan Özgür, Suzan Üsküdarli:
BOUNCE: Sentiment Classification in Twitter using Rich Feature Sets. 554-561 - Prodromos Malakasiotis, Rafael-Michael Karampatsis, Konstantina Makrynioti, John Pavlopoulos:
nlp.cs.aueb.gr: Two Stage Sentiment Analysis. 562-567 - Pedro Balage Filho, Thiago Alexandre Salgueiro Pardo:
NILC_USP: A Hybrid System for Sentiment Analysis in Twitter Messages. 568-572 - Emanuele Bastianelli, Danilo Croce, Roberto Basili, Daniele Nardi:
UNITOR-HMM-TK: Structured Kernel-based learning for Spatial Role Labeling. 573-579 - Itziar Aldabe, Montse Maritxalar, Oier Lopez de Lacalle:
EHU-ALM: Similarity-Feature Based Approach for Student Response Analysis. 580-584 - Ergun Biçici, Josef van Genabith:
CNGL: Grading Student Answers by Acts of Translation. 585-591 - Milen Kouylekov, Luca Dini, Alessio Bosca, Marco Trevisan:
Celi: EDITS and Generic Text Pair Classification. 592-597 - Martin Gleize, Brigitte Grau:
LIMSIILES: Basic English Substitution for Student Answer Assessment at SemEval 2013. 598-602 - Ifeyinwa Okoye, Steven Bethard, Tamara Sumner:
CU : Computational Assessment of Short Free Text Answers - A Tool for Evaluating Students' Understanding. 603-607 - Niels Ott, Ramon Ziai, Michael Hahn, Detmar Meurers:
CoMeT: Integrating different levels of linguistic modeling for meaning assessment. 608-616 - Daniel Sánchez-Cisneros:
UC3M: A kernel-based approach to identify and classify DDIs in bio-medical texts. 617-621 - Daniel Sánchez-Cisneros, Fernando Aparicio Gali:
UEM-UC3M: An Ontology-based named entity recognition system for biomedical texts. 622-627 - Philippe Thomas, Mariana L. Neves, Tim Rocktäschel, Ulf Leser:
WBI-DDI: Drug-Drug Interaction Extraction using Majority Voting. 628-635 - Armando Collazo, Alberto Ceballo, Dennys D. Puig, Yoan Gutiérrez, José Ignacio Abreu, Roger Pérez, Antonio Fernández Orquín, Andrés Montoyo, Rafael Muñoz, Franc Camara:
UMCC_DLSI: Semantic and Lexical features for detection and classification Drugs in biomedical texts. 636-643 - Behrouz Bokharaeian, Alberto Díaz:
NIL_UCM: Extracting Drug-Drug interactions from text through combination of sequence and tree kernels. 644-650 - Jari Björne, Suwisa Kaewphan, Tapio Salakoski:
UTurku: Drug Named Entity Recognition and Drug-Drug Interaction Extraction Using SVM Classification and Domain Knowledge. 651-659 - Tiago Grego, Francisco R. Pinto, Francisco M. Couto:
LASIGE: using Conditional Random Fields and ChEBI ontology. 660-666 - Majid Rastegar-Mojarad, Richard D. Boyce, Rashmi Prasad:
UWM-TRIADS: Classifying Drug-Drug Interactions with Two-Stage SVM and Post-Processing. 667-674 - Tamara Bobic, Juliane Fluck, Martin Hofmann-Apitius:
SCAI: Extracting drug-drug interactions using a rich feature vector. 675-683 - Negacy D. Hailu, Lawrence E. Hunter, K. Bretonnel Cohen:
UColorado_SOM: Extraction of Drug-Drug Interactions from Biomedical Text using Knowledge-rich and Knowledge-poor Features. 684-688 - David Richard Hope, Bill Keller:
UoS: A Graph-Based System for Graded Word Sense Induction. 689-694
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
last updated on 2025-02-10 21:33 CET by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
