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Front matter |
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SemEval-2016 Task 4: Sentiment Analysis in Twitter Preslav Nakov, Alan Ritter, Sara Rosenthal, Fabrizio Sebastiani and Veselin Stoyanov |
pp. 1–18 |
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SemEval-2016 Task 5: Aspect Based Sentiment Analysis Maria Pontiki, Dimitris Galanis, Haris Papageorgiou, Ion Androutsopoulos, Suresh Manandhar, Mohammad AL-Smadi, Mahmoud Al-Ayyoub, Yanyan Zhao, Bing Qin, Orphee De Clercq, Veronique Hoste, Marianna Apidianaki, Xavier Tannier, Natalia Loukachevitch, Evgeniy Kotelnikov, Núria Bel, Salud María Jiménez-Zafra and Gülşen Eryiğit |
pp. 19–30 |
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SemEval-2016 Task 6: Detecting Stance in Tweets Saif Mohammad, Svetlana Kiritchenko, Parinaz Sobhani, Xiaodan Zhu and Colin Cherry |
pp. 31–41 |
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SemEval-2016 Task 7: Determining Sentiment Intensity of English and Arabic Phrases Svetlana Kiritchenko, Saif Mohammad and Mohammad Salameh |
pp. 42–51 |
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CUFE at SemEval-2016 Task 4: A Gated Recurrent Model for Sentiment Classification Mahmoud Nabil, Amir Atyia and Mohamed Aly |
pp. 52–57 |
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QCRI at SemEval-2016 Task 4: Probabilistic Methods for Binary and Ordinal Quantification Giovanni Da San Martino, Wei Gao and Fabrizio Sebastiani |
pp. 58–63 |
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SteM at SemEval-2016 Task 4: Applying Active Learning to Improve Sentiment Classification Stefan Räbiger, Mishal Kazmi, Yücel Saygın, Peter Schüller and Myra Spiliopoulou |
pp. 64–70 |
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I2RNTU at SemEval-2016 Task 4: Classifier Fusion for Polarity Classification in Twitter Zhengchen Zhang, Chen Zhang, wu fuxiang, Dongyan Huang, Weisi Lin and Minghui Dong |
pp. 71–78 |
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LyS at SemEval-2016 Task 4: Exploiting Neural Activation Values for Twitter Sentiment Classification and Quantification David Vilares, Yerai Doval, Miguel A. Alonso and Carlos Gómez-Rodríguez |
pp. 79–84 |
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TwiSE at SemEval-2016 Task 4: Twitter Sentiment Classification Georgios Balikas and Massih-Reza Amini |
pp. 85–91 |
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ISTI-CNR at SemEval-2016 Task 4: Quantification on an Ordinal Scale Andrea Esuli |
pp. 92–95 |
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aueb.twitter.sentiment at SemEval-2016 Task 4: A Weighted Ensemble of SVMs for Twitter Sentiment Analysis Stavros Giorgis, Apostolos Rousas, John Pavlopoulos, Prodromos Malakasiotis and Ion Androutsopoulos |
pp. 96–99 |
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thecerealkiller at SemEval-2016 Task 4: Deep Learning based System for Classifying Sentiment of Tweets on Two Point Scale Vikrant Yadav |
pp. 100–102 |
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NTNUSentEval at SemEval-2016 Task 4: Combining General Classifiers for Fast Twitter Sentiment Analysis Brage Ekroll Jahren, Valerij Fredriksen, Björn Gambäck and Lars Bungum |
pp. 103–108 |
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UDLAP at SemEval-2016 Task 4: Sentiment Quantification Using a Graph Based Representation Esteban Castillo, Ofelia Cervantes, Darnes Vilariño and David Báez |
pp. 109–114 |
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GTI at SemEval-2016 Task 4: Training a Naive Bayes Classifier using Features of an Unsupervised System Jonathan Juncal-Martínez, Tamara Álvarez-López, Milagros Fernández-Gavilanes, Enrique Costa-Montenegro and Francisco Javier González-Castaño |
pp. 115–119 |
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Aicyber at SemEval-2016 Task 4: i-vector based sentence representation Steven Du and Xi Zhang |
pp. 120–125 |
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SwissCheese at SemEval-2016 Task 4: Sentiment Classification Using an Ensemble of Convolutional Neural Networks with Distant Supervision Jan Deriu, Maurice Gonzenbach, Fatih Uzdilli, Aurelien Lucchi, Valeria De Luca and Martin Jaggi |
pp. 126–130 |
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PUT at SemEval-2016 Task 4: The ABC of Twitter Sentiment Analysis Mateusz Lango, Dariusz Brzezinski and Jerzy Stefanowski |
pp. 131–137 |
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mib at SemEval-2016 Task 4a: Exploiting lexicon based features for Sentiment Analysis in Twitter Vittoria Cozza and Marinella Petrocchi |
pp. 138–143 |
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MDSENT at SemEval-2016 Task 4: A Supervised System for Message Polarity Classification Hang Gao and Tim Oates |
pp. 144–149 |
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CICBUAPnlp at SemEval-2016 Task 4-A: Discovering Twitter Polarity using Enhanced Embeddings Helena Gomez, Darnes Vilariño, Grigori Sidorov and David Pinto Avendaño |
pp. 150–153 |
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Finki at SemEval-2016 Task 4: Deep Learning Architecture for Twitter Sentiment Analysis Dario Stojanovski, Gjorgji Strezoski, Gjorgji Madjarov and Ivica Dimitrovski |
pp. 154–159 |
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Tweester at SemEval-2016 Task 4: Sentiment Analysis in Twitter Using Semantic-Affective Model Adaptation Elisavet Palogiannidi, Athanasia Kolovou, Fenia Christopoulou, Filippos Kokkinos, Elias Iosif, Nikolaos Malandrakis, Haris Papageorgiou, Shrikanth Narayanan and Alexandros Potamianos |
pp. 160–168 |
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UofL at SemEval-2016 Task 4: Multi Domain word2vec for Twitter Sentiment Classification Omar Abdelwahab and Adel Elmaghraby |
pp. 169–175 |
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NRU-HSE at SemEval-2016 Task 4: Comparative Analysis of Two Iterative Methods Using Quantification Library Nikolay Karpov, Alexander Porshnev and Kirill Rudakov |
pp. 176–182 |
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INSIGHT-1 at SemEval-2016 Task 4: Convolutional Neural Networks for Sentiment Classification and Quantification Sebastian Ruder, Parsa Ghaffari and John G. Breslin |
pp. 183–187 |
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UNIMELB at SemEval-2016 Tasks 4A and 4B: An Ensemble of Neural Networks and a Word2Vec Based Model for Sentiment Classification Steven Xu, HuiZhi Liang and Timothy Baldwin |
pp. 188–194 |
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SentiSys at SemEval-2016 Task 4: Feature-Based System for Sentiment Analysis in Twitter Hussam Hamdan |
pp. 195–202 |
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DSIC-ELIRF at SemEval-2016 Task 4: Message Polarity Classification in Twitter using a Support Vector Machine Approach Victor Martinez Morant, Lluís-F Hurtado and Ferran Pla |
pp. 203–206 |
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SENSEI-LIF at SemEval-2016 Task 4: Polarity embedding fusion for robust sentiment analysis Mickael Rouvier and Benoit Favre |
pp. 207–213 |
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DiegoLab16 at SemEval-2016 Task 4: Sentiment Analysis in Twitter using Centroids, Clusters, and Sentiment Lexicons Abeed Sarker and Graciela Gonzalez |
pp. 214–219 |
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VCU-TSA at Semeval-2016 Task 4: Sentiment Analysis in Twitter Gerard Briones, Kasun Amarasinghe and Bridget McInnes |
pp. 220–224 |
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UniPI at SemEval-2016 Task 4: Convolutional Neural Networks for Sentiment Classification Giuseppe Attardi and Daniele Sartiano |
pp. 225–229 |
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IIP at SemEval-2016 Task 4: Prioritizing Classes in Ensemble Classification for Sentiment Analysis of Tweets Jasper Friedrichs |
pp. 230–234 |
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PotTS at SemEval-2016 Task 4: Sentiment Analysis of Twitter Using Character-level Convolutional Neural Networks. Uladzimir Sidarenka |
pp. 235–242 |
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INESC-ID at SemEval-2016 Task 4-A: Reducing the Problem of Out-of-Embedding Words Silvio Amir, Ramón Astudillo, Wang Ling, Mario J. Silva and Isabel Trancoso |
pp. 243–247 |
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SentimentalITsts at SemEval-2016 Task 4: building a Twitter sentiment analyzer in your backyard Cosmin Florean, Oana Bejenaru, Eduard Apostol, Octavian Ciobanu, Adrian Iftene and Diana Trandabat |
pp. 248–251 |
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Minions at SemEval-2016 Task 4: or how to build a sentiment analyzer using off-the-shelf resources? Calin-Cristian Ciubotariu, Marius-Valentin Hrisca, Mihail Gliga, Diana Darabana, Diana Trandabat and Adrian Iftene |
pp. 252–255 |
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YZU-NLP Team at SemEval-2016 Task 4: Ordinal Sentiment Classification Using a Recurrent Convolutional Network Yunchao He, Liang-Chih Yu, Chin-Sheng Yang, K. Robert Lai and Weiyi Liu |
pp. 256–260 |
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ECNU at SemEval-2016 Task 4: An Empirical Investigation of Traditional NLP Features and Word Embedding Features for Sentence-level and Topic-level Sentiment Analysis in Twitter Yunxiao Zhou, Zhihua Zhang and Man Lan |
pp. 261–266 |
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OPAL at SemEval-2016 Task 4: the Challenge of Porting a Sentiment Analysis System to the "Real" World Alexandra Balahur |
pp. 267–270 |
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Know-Center at SemEval-2016 Task 5: Using Word Vectors with Typed Dependencies for Opinion Target Expression Extraction Stefan Falk, Andi Rexha and Roman Kern |
pp. 271–275 |
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NileTMRG at SemEval-2016 Task 5: Deep Convolutional Neural Networks for Aspect Category and Sentiment Extraction Talaat Khalil and Samhaa R. El-Beltagy |
pp. 276–281 |
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XRCE at SemEval-2016 Task 5: Feedbacked Ensemble Modeling on Syntactico-Semantic Knowledge for Aspect Based Sentiment Analysis Caroline Brun, Julien Perez and Claude Roux |
pp. 282–286 |
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NLANGP at SemEval-2016 Task 5: Improving Aspect Based Sentiment Analysis using Neural Network Features Zhiqiang Toh and Jian Su |
pp. 287–293 |
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bunji at SemEval-2016 Task 5: Neural and Syntactic Models of Entity-Attribute Relationship for Aspect-based Sentiment Analysis Toshihiko Yanase, Kohsuke Yanai, Misa Sato, Toshinori Miyoshi and Yoshiki Niwa |
pp. 294–300 |
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IHS-RD-Belarus at SemEval-2016 Task 5: Detecting Sentiment Polarity Using the Heatmap of Sentence Maryna Chernyshevich |
pp. 301–305 |
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BUTknot at SemEval-2016 Task 5: Supervised Machine Learning with Term Substitution Approach in Aspect Category Detection Jakub Machacek |
pp. 306–310 |
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IIT-TUDA at SemEval-2016 Task 5: Beyond Sentiment Lexicon: Combining Domain Dependency and Distributional Semantics Features for Aspect Based Sentiment Analysis Ayush Kumar, Sarah Kohail, Amit Kumar, Asif Ekbal and Chris Biemann |
pp. 311–317 |
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GTI at SemEval-2016 Task 5: SVM and CRF for Aspect Detection and Unsupervised Aspect-Based Sentiment Analysis Tamara Álvarez-López, Jonathan Juncal-Martínez, Milagros Fernández-Gavilanes, Enrique Costa-Montenegro and Francisco Javier González-Castaño |
pp. 318–323 |
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AUEB-ABSA at SemEval-2016 Task 5: Ensembles of Classifiers and Embeddings for Aspect Based Sentiment Analysis Dionysios Xenos, Panagiotis Theodorakakos, John Pavlopoulos, Prodromos Malakasiotis and Ion Androutsopoulos |
pp. 324–329 |
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AKTSKI at SemEval-2016 Task 5: Aspect Based Sentiment Analysis for Consumer Reviews Shubham Pateria and Prafulla Choubey |
pp. 330–336 |
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MayAnd at SemEval-2016 Task 5: Syntactic and word2vec-based approach to aspect-based polarity detection in Russian Vladimir Mayorov and Ivan Andrianov |
pp. 337–341 |
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INSIGHT-1 at SemEval-2016 Task 5: Deep Learning for Multilingual Aspect-based Sentiment Analysis Sebastian Ruder, Parsa Ghaffari and John G. Breslin |
pp. 342–348 |
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TGB at SemEval-2016 Task 5: Multi-Lingual Constraint System for Aspect Based Sentiment Analysis Fatih Samet Çetin, Ezgi Yıldırım, Can Özbey and Gülşen Eryiğit |
pp. 349–353 |
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UWB at SemEval-2016 Task 5: Aspect Based Sentiment Analysis Tomáš Hercig, Tomáš Brychcín, Lukáš Svoboda and Michal Konkol |
pp. 354–361 |
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SentiSys at SemEval-2016 Task 5: Opinion Target Extraction and Sentiment Polarity Detection Hussam Hamdan |
pp. 362–367 |
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COMMIT at SemEval-2016 Task 5: Sentiment Analysis with Rhetorical Structure Theory Kim Schouten and Flavius Frasincar |
pp. 368–372 |
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ECNU at SemEval-2016 Task 5: Extracting Effective Features from Relevant Fragments in Sentence for Aspect-Based Sentiment Analysis in Reviews Mengxiao Jiang, Zhihua Zhang and Man Lan |
pp. 373–378 |
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UFAL at SemEval-2016 Task 5: Recurrent Neural Networks for Sentence Classification Aleš Tamchyna and Kateřina Veselovská |
pp. 379–383 |
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UWaterloo at SemEval-2016 Task 5: Minimally Supervised Approaches to Aspect-Based Sentiment Analysis Olga Vechtomova and Anni He |
pp. 384–389 |
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INF-UFRGS-OPINION-MINING at SemEval-2016 Task 6: Automatic Generation of a Training Corpus for Unsupervised Identification of Stance in Tweets Marcelo Dias and Karin Becker |
pp. 390–395 |
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pkudblab at SemEval-2016 Task 6 : A Specific Convolutional Neural Network System for Effective Stance Detection Wan Wei, Xiao Zhang, Xuqin Liu, Wei Chen and Tengjiao Wang |
pp. 396–400 |
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USFD at SemEval-2016 Task 6: Any-Target Stance Detection on Twitter with Autoencoders Isabelle Augenstein, Andreas Vlachos and Kalina Bontcheva |
pp. 401–405 |
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IUCL at SemEval-2016 Task 6: An Ensemble Model for Stance Detection in Twitter Can Liu, Wen Li, Bradford Demarest, Yue Chen, Sara Couture, Daniel Dakota, Nikita Haduong, Noah Kaufman, Andrew Lamont, Manan Pancholi, Kenneth Steimel and Sandra Kübler |
pp. 406–412 |
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Tohoku at SemEval-2016 Task 6: Feature-based Model versus Convolutional Neural Network for Stance Detection Yuki Igarashi, Hiroya Komatsu, Sosuke Kobayashi, Naoaki Okazaki and Kentaro Inui |
pp. 413–419 |
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UWB at SemEval-2016 Task 6: Stance Detection Peter Krejzl and Josef Steinberger |
pp. 420–424 |
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DeepStance at SemEval-2016 Task 6: Detecting Stance in Tweets Using Character and Word-Level CNNs Prashanth Vijayaraghavan, Ivan Sysoev, Soroush Vosoughi and Deb Roy |
pp. 425–431 |
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NLDS-UCSC at SemEval-2016 Task 6: A Semi-Supervised Approach to Detecting Stance in Tweets Amita Misra, Brian Ecker, Theodore Handleman, Nicolas Hahn and Marilyn Walker |
pp. 432–439 |
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ltl.uni-due at SemEval-2016 Task 6: Stance Detection in Social Media Using Stacked Classifiers Michael Wojatzki and Torsten Zesch |
pp. 440–445 |
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CU-GWU Perspective at SemEval-2016 Task 6: Ideological Stance Detection in Informal Text Heba Elfardy and Mona Diab |
pp. 446–451 |
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JU_NLP at SemEval-2016 Task 6: Detecting Stance in Tweets using Support Vector Machines Braja Gopal Patra, Dipankar Das and Sivaji Bandyopadhyay |
pp. 452–456 |
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IDI@NTNU at SemEval-2016 Task 6: Detecting Stance in Tweets Using Shallow Features and GloVe Vectors for Word Representation Henrik Bøhler, Petter Asla, Erwin Marsi and Rune Sætre |
pp. 457–462 |
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ECNU at SemEval 2016 Task 6: Relevant or Not? Supportive or Not? A Two-step Learning System for Automatic Detecting Stance in Tweets Zhihua Zhang and Man Lan |
pp. 463–469 |
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MITRE at SemEval-2016 Task 6: Transfer Learning for Stance Detection Guido Zarrella and Amy Marsh |
pp. 470–475 |
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TakeLab at SemEval-2016 Task 6: Stance Classification in Tweets Using a Genetic Algorithm Based Ensemble Martin Tutek, Ivan Sekulic, Paula Gombar, Ivan Paljak, Filip Culinovic, Filip Boltuzic, Mladen Karan, Domagoj Alagić and Jan Šnajder |
pp. 476–480 |
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LSIS at SemEval-2016 Task 7: Using Web Search Engines for English and Arabic Unsupervised Sentiment Intensity Prediction Amal Htait, Sebastien Fournier and Patrice Bellot |
pp. 481–485 |
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iLab-Edinburgh at SemEval-2016 Task 7: A Hybrid Approach for Determining Sentiment Intensity of Arabic Twitter Phrases Eshrag Refaee and Verena Rieser |
pp. 486–492 |
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UWB at SemEval-2016 Task 7: Novel Method for Automatic Sentiment Intensity Determination Ladislav Lenc, Pavel Král and Václav Rajtmajer |
pp. 493–497 |
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NileTMRG at SemEval-2016 Task 7: Deriving Prior Polarities for Arabic Sentiment Terms Samhaa R. El-Beltagy |
pp. 498–502 |
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ECNU at SemEval-2016 Task 7: An Enhanced Supervised Learning Method for Lexicon Sentiment Intensity Ranking Feixiang Wang, Zhihua Zhang and Man Lan |
pp. 503–508 |
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SemEval-2016 Task 1: Semantic Textual Similarity, Monolingual and Cross-Lingual Evaluation Eneko Agirre, Carmen Banea, Daniel Cer, Mona Diab, Aitor Gonzalez-Agirre, Rada Mihalcea, German Rigau and Janyce Wiebe |
pp. 509–523 |
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SemEval-2016 Task 2: Interpretable Semantic Textual Similarity Eneko Agirre, Aitor Gonzalez-Agirre, Inigo Lopez-Gazpio, Montse Maritxalar, German Rigau and Larraitz Uria |
pp. 524–536 |
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SemEval-2016 Task 3: Community Question Answering Preslav Nakov, Lluís Màrquez, Alessandro Moschitti, Walid Magdy, Hamdy Mubarak, abed Alhakim Freihat, Jim Glass and Bilal Randeree |
pp. 537–557 |
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SemEval-2016 Task 10: Detecting Minimal Semantic Units and their Meanings (DiMSUM) Nathan Schneider, Dirk Hovy, Anders Johannsen and Marine Carpuat |
pp. 558–571 |
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SemEval 2016 Task 11: Complex Word Identification Gustavo Paetzold and Lucia Specia |
pp. 572–581 |
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FBK HLT-MT at SemEval-2016 Task 1: Cross-lingual Semantic Similarity Measurement Using Quality Estimation Features and Compositional Bilingual Word Embeddings Duygu Ataman, Jose G. C. De Souza, Marco Turchi and Matteo Negri |
pp. 582–588 |
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VRep at SemEval-2016 Task 1 and Task 2: A System for Interpretable Semantic Similarity Sam Henry and Allison Sands |
pp. 589–595 |
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UTA DLNLP at SemEval-2016 Task 1: Semantic Textual Similarity: A Unified Framework for Semantic Processing and Evaluation Peng Li and Heng Huang |
pp. 596–599 |
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UWB at SemEval-2016 Task 1: Semantic Textual Similarity using Lexical, Syntactic, and Semantic Information Tomáš Brychcín and Lukáš Svoboda |
pp. 600–606 |
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HHU at SemEval-2016 Task 1: Multiple Approaches to Measuring Semantic Textual Similarity Matthias Liebeck, Philipp Pollack, Pashutan Modaresi and Stefan Conrad |
pp. 607–613 |
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Samsung Poland NLP Team at SemEval-2016 Task 1: Necessity for diversity; combining recursive autoencoders, WordNet and ensemble methods to measure semantic similarity. Barbara Rychalska, Katarzyna Pakulska, Krystyna Chodorowska, Wojciech Walczak and Piotr Andruszkiewicz |
pp. 614–620 |
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USFD at SemEval-2016 Task 1: Putting different State-of-the-Arts into a Box Ahmet Aker, Frederic Blain, Andres Duque, Marina Fomicheva, Jurica Seva, Kashif Shah and Daniel Beck |
pp. 621–625 |
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NaCTeM at SemEval-2016 Task 1: Inferring sentence-level semantic similarity from an ensemble of complementary lexical and sentence-level features Piotr Przybyła, Nhung T. H. Nguyen, Matthew Shardlow, Georgios Kontonatsios and Sophia Ananiadou |
pp. 626–632 |
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ECNU at SemEval-2016 Task 1: Leveraging Word Embedding From Macro and Micro Views to Boost Performance for Semantic Textual Similarity Junfeng Tian and Man Lan |
pp. 633–639 |
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SAARSHEFF at SemEval-2016 Task 1: Semantic Textual Similarity with Machine Translation Evaluation Metrics and (eXtreme) Boosted Tree Ensembles Liling Tan, Carolina Scarton, Lucia Specia and Josef van Genabith |
pp. 640–645 |
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WOLVESAAR at SemEval-2016 Task 1: Replicating the Success of Monolingual Word Alignment and Neural Embeddings for Semantic Textual Similarity Hannah Bechara, Rohit Gupta, Liling Tan, Constantin Orasan, Ruslan Mitkov and Josef van Genabith |
pp. 646–651 |
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DTSim at SemEval-2016 Task 1: Semantic Similarity Model Including Multi-Level Alignment and Vector-Based Compositional Semantics Rajendra Banjade, Nabin Maharjan, Dipesh Gautam and Vasile Rus |
pp. 652–656 |
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ISCAS_NLP at SemEval-2016 Task 1: Sentence Similarity Based on Support Vector Regression using Multiple Features Cheng Fu, Bo An, Xianpei Han and Le Sun |
pp. 657–661 |
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UMD-TTIC-UW at SemEval-2016 Task 1: Attention-Based Multi-Perspective Convolutional Neural Networks for Textual Similarity Measurement Hua He, John Wieting, Kevin Gimpel, Jinfeng Rao and Jimmy Lin |
pp. 662–667 |
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DLS@CU at SemEval-2016 Task 1: Supervised Models of Sentence Similarity Md Arafat Sultan, Steven Bethard and Tamara Sumner |
pp. 668–673 |
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DCU-SEManiacs at SemEval-2016 Task 1: Synthetic Paragram Embeddings for Semantic Textual Similarity Chris Hokamp and Piyush Arora |
pp. 674–680 |
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iUBC at SemEval-2016 Task 2: RNNs and LSTMs for interpretable STS Inigo Lopez-Gazpio, Eneko Agirre and Montse Maritxalar |
pp. 681–686 |
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Rev at SemEval-2016 Task 2: Aligning Chunks by Lexical, Part of Speech and Semantic Equivalence ping tan, Karin Verspoor and Timothy Miller |
pp. 687–692 |
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Inspire at SemEval-2016 Task 2: Interpretable Semantic Textual Similarity Alignment based on Answer Set Programming Mishal Kazmi and Peter Schüller |
pp. 693–699 |
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FBK-HLT-NLP at SemEval-2016 Task 2: A Multitask, Deep Learning Approach for Interpretable Semantic Textual Similarity Simone Magnolini, Anna Feltracco and Bernardo Magnini |
pp. 700–706 |
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IISCNLP at SemEval-2016 Task 2: Interpretable STS with ILP based Multiple Chunk Aligner Lavanya Tekumalla and Sharmistha Jat |
pp. 707–712 |
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VENSESEVAL at Semeval-2016 Task 2 iSTS - with a full-fledged rule-based approach Rodolfo Delmonte |
pp. 713–719 |
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UWB at SemEval-2016 Task 2: Interpretable Semantic Textual Similarity with Distributional Semantics for Chunks Miloslav Konopik, Ondrej Prazak, David Steinberger and Tomáš Brychcín |
pp. 720–725 |
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DTSim at SemEval-2016 Task 2: Interpreting Similarity of Texts Based on Automated Chunking, Chunk Alignment and Semantic Relation Prediction Rajendra Banjade, Nabin Maharjan, Nobal Bikram Niraula and Vasile Rus |
pp. 726–730 |
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UH-PRHLT at SemEval-2016 Task 3: Combining Lexical and Semantic-based Features for Community Question Answering Marc Franco-Salvador, Sudipta Kar, Thamar Solorio and Paolo Rosso |
pp. 731–738 |
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RDI_Team at SemEval-2016 Task 3: RDI Unsupervised Framework for Text Ranking Ahmed Magooda, Amr Gomaa, Ashraf Mahgoub, Hany Ahmed, Mohsen Rashwan, Hazem Raafat, Eslam Kamal and Ahmad Al Sallab |
pp. 739–744 |
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KeLP at SemEval-2016 Task 3: Learning Semantic Relations between Questions and Answers Simone Filice, Danilo Croce, Alessandro Moschitti and Roberto Basili |
pp. 745–752 |
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SLS at SemEval-2016 Task 3: Neural-based Approaches for Ranking in Community Question Answering Mitra Mohtarami, Yonatan Belinkov, Wei-Ning Hsu, Yu Zhang, Tao Lei, Kfir Bar, Scott Cyphers and Jim Glass |
pp. 753–760 |
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SUper Team at SemEval-2016 Task 3: Building a Feature-Rich System for Community Question Answering Tsvetomila Mihaylova, Pepa Gencheva, Martin Boyanov, Ivana Yovcheva, Todor Mihaylov, Momchil Hardalov, Yasen Kiprov, Daniel Balchev, Ivan Koychev, Preslav Nakov, Ivelina Nikolova and Galia Angelova |
pp. 761–768 |
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PMI-cool at SemEval-2016 Task 3: Experiments with PMI and Goodness Polarity Lexicons for Community Question Answering Daniel Balchev, Yasen Kiprov, Ivan Koychev and Preslav Nakov |
pp. 769–775 |
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UniMelb at SemEval-2016 Task 3: Identifying Similar Questions by combining a CNN with String Similarity Measures Timothy Baldwin, Huizhi Liang, Bahar Salehi, Doris Hoogeveen, Yitong Li and Long Duong |
pp. 776–781 |
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ICL00 at SemEval-2016 Task 3: Translation-Based Method for CQA System Yunfang Wu and Minghua Zhang |
pp. 782–785 |
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Overfitting at SemEval-2016 Task 3: Detecting Semantically Similar Questions in Community Question Answering Forums with Word Embeddings Hujie Wang and Pascal Poupart |
pp. 786–790 |
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QU-IR at SemEval 2016 Task 3: Learning to Rank on Arabic Community Question Answering Forums with Word Embedding Rana Malhas, Marwan Torki and Tamer Elsayed |
pp. 791–796 |
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ECNU at SemEval-2016 Task 3: Exploring Traditional Method and Deep Learning Method for Question Retrieval and Answer Ranking in Community Question Answering Guoshun Wu and Man Lan |
pp. 797–803 |
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SemanticZ at SemEval-2016 Task 3: Ranking Relevant Answers in Community Question Answering Using Semantic Similarity Based on Fine-tuned Word Embeddings Todor Mihaylov and Preslav Nakov |
pp. 804–811 |
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MTE-NN at SemEval-2016 Task 3: Can Machine Translation Evaluation Help Community Question Answering? Francisco Guzmán, Preslav Nakov and Lluís Màrquez |
pp. 812–820 |
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ConvKN at SemEval-2016 Task 3: Answer and Question Selection for Question Answering on Arabic and English Fora Alberto Barrón-Cedeño, Giovanni Da San Martino, Shafiq Joty, Alessandro Moschitti, Fahad Al-Obaidli, Salvatore Romeo, Kateryna Tymoshenko and Antonio Uva |
pp. 821–828 |
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ITNLP-AiKF at SemEval-2016 Task 3 a quesiton answering system using community QA repository Chang e Jia |
pp. 829–834 |
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UFRGS&LIF at SemEval-2016 Task 10: Rule-Based MWE Identification and Predominant-Supersense Tagging Silvio Cordeiro, Carlos Ramisch and Aline Villavicencio |
pp. 835–842 |
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WHUNlp at SemEval-2016 Task DiMSUM: A Pilot Study in Detecting Minimal Semantic Units and their Meanings using Supervised Models Xin Tang, Fei Li and Donghong Ji |
pp. 843–849 |
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UTU at SemEval-2016 Task 10: Binary Classification for Expression Detection (BCED) Jari Björne and Tapio Salakoski |
pp. 850–855 |
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UW-CSE at SemEval-2016 Task 10: Detecting Multiword Expressions and Supersenses using Double-Chained Conditional Random Fields Mohammad Javad Hosseini, Noah A. Smith and Su-In Lee |
pp. 856–861 |
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ICL-HD at SemEval-2016 Task 10: Improving the Detection of Minimal Semantic Units and their Meanings with an Ontology and Word Embeddings Angelika Kirilin, Felix Krauss and Yannick Versley |
pp. 862–870 |
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VectorWeavers at SemEval-2016 Task 10: From Incremental Meaning to Semantic Unit (phrase by phrase) Andreas Scherbakov, Ekaterina Vylomova, Fei Liu and Timothy Baldwin |
pp. 871–877 |
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PLUJAGH at SemEval-2016 Task 11: Simple System for Complex Word Identification Krzysztof Wróbel |
pp. 878–882 |
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USAAR at SemEval-2016 Task 11: Complex Word Identification with Sense Entropy and Sentence Perplexity José Manuel Martínez Martínez and Liling Tan |
pp. 883–887 |
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Sensible at SemEval-2016 Task 11: Neural Nonsense Mangled in Ensemble Mess Gillin Nat |
pp. 888–893 |
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SV000gg at SemEval-2016 Task 11: Heavy Gauge Complex Word Identification with System Voting Gustavo Paetzold and Lucia Specia |
pp. 894–899 |
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Melbourne at SemEval 2016 Task 11: Classifying Type-level Word Complexity using Random Forests with Corpus and Word List Features Julian Brooke, Alexandra Uitdenbogerd and Timothy Baldwin |
pp. 900–906 |
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CLaC at SemEval-2016 Task 11: Exploring linguistic and psycho-linguistic Features for Complex Word Identification Elnaz Davoodi and Leila Kosseim |
pp. 907–910 |
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JU_NLP at SemEval-2016 Task 11: Identifying Complex Words in a Sentence Niloy Mukherjee, Braja Gopal Patra, Dipankar Das and Sivaji Bandyopadhyay |
pp. 911–915 |
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MAZA at SemEval-2016 Task 11: Detecting Lexical Complexity Using a Decision Stump Meta-Classifier Shervin Malmasi and Marcos Zampieri |
pp. 916–920 |
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LTG at SemEval-2016 Task 11: Complex Word Identification with Classifier Ensembles Shervin Malmasi, Mark Dras and Marcos Zampieri |
pp. 921–925 |
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MacSaar at SemEval-2016 Task 11: Zipfian and Character Features for ComplexWord Identification Marcos Zampieri, Liling Tan and Josef van Genabith |
pp. 926–930 |
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Garuda & Bhasha at SemEval-2016 Task 11: Complex Word Identification Using Aggregated Learning Models Prafulla Choubey and Shubham Pateria |
pp. 931–935 |
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TALN at SemEval-2016 Task 11: Modelling Complex Words by Contextual, Lexical and Semantic Features Francesco Ronzano, Ahmed Abura’ed, Luis Espinosa Anke and Horacio Saggion |
pp. 936–941 |
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IIIT at SemEval-2016 Task 11: Complex Word Identification using Nearest Centroid Classification Ashish Palakurthi and Radhika Mamidi |
pp. 942–946 |
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AmritaCEN at SemEval-2016 Task 11: Complex Word Identification using Word Embedding sanjay sp, Anand Kumar and Soman K P |
pp. 947–952 |
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CoastalCPH at SemEval-2016 Task 11: The importance of designing your Neural Networks right Joachim Bingel, Natalie Schluter and Héctor Martínez Alonso |
pp. 953–958 |
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HMC at SemEval-2016 Task 11: Identifying Complex Words Using Depth-limited Decision Trees Maury Quijada and Julie Medero |
pp. 959–962 |
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UWB at SemEval-2016 Task 11: Exploring Features for Complex Word Identification Michal Konkol |
pp. 963–966 |
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AI-KU at SemEval-2016 Task 11: Word Embeddings and Substring Features for Complex Word Identification Onur Kuru |
pp. 967–971 |
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Pomona at SemEval-2016 Task 11: Predicting Word Complexity Based on Corpus Frequency David Kauchak |
pp. 972–976 |
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SemEval-2016 Task 12: Clinical TempEval Steven Bethard, Guergana Savova, Wei-Te Chen, Leon Derczynski, James Pustejovsky and Marc Verhagen |
pp. 977–987 |
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SemEval-2016 Task 8: Meaning Representation Parsing Jonathan May |
pp. 988–997 |
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SemEval-2016 Task 9: Chinese Semantic Dependency Parsing Wanxiang Che, Yanqiu Shao, Ting Liu and Yu Ding |
pp. 998–1004 |
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SemEval-2016 Task 13: Taxonomy Extraction Evaluation (TExEval-2) Georgeta Bordea, Els Lefever and Paul Buitelaar |
pp. 1005–1015 |
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SemEval-2016 Task 14: Semantic Taxonomy Enrichment David Jurgens and Mohammad Taher Pilehvar |
pp. 1016–1026 |
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LIMSI-COT at SemEval-2016 Task 12: Temporal relation identification using a pipeline of classifiers Julien Tourille, Olivier Ferret, Aurélie Névéol and Xavier Tannier |
pp. 1027–1033 |
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RIGA at SemEval-2016 Task 8: Impact of Smatch Extensions and Character-Level Neural Translation on AMR Parsing Accuracy Guntis Barzdins and Didzis Gosko |
pp. 1034–1038 |
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DynamicPower at SemEval-2016 Task 8: Processing syntactic parse trees with a Dynamic Semantics core Alastair Butler |
pp. 1039–1044 |
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M2L at SemEval-2016 Task 8: AMR Parsing with Neural Networks Yevgeniy Puzikov, Daisuke Kawahara and Sadao Kurohashi |
pp. 1045–1050 |
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ICL-HD at SemEval-2016 Task 8: Meaning Representation Parsing - Augmenting AMR Parsing with a Preposition Semantic Role Labeling Neural Network Lauritz Brandt, David Grimm, Mengfei Zhou and Yannick Versley |
pp. 1051–1057 |
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UCL+Sheffield at SemEval-2016 Task 8: Imitation learning for AMR parsing with an alpha-bound James Goodman, Andreas Vlachos and Jason Naradowsky |
pp. 1058–1063 |
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CAMR at SemEval-2016 Task 8: An Extended Transition-based AMR Parser Chuan Wang, Sameer Pradhan, Xiaoman Pan, Heng Ji and Nianwen Xue |
pp. 1064–1069 |
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The Meaning Factory at SemEval-2016 Task 8: Producing AMRs with Boxer Johannes Bjerva, Johan Bos and Hessel Haagsma |
pp. 1070–1075 |
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UofR at SemEval-2016 Task 8: Learning Synchronous Hyperedge Replacement Grammar for AMR Parsing Xiaochang Peng and Daniel Gildea |
pp. 1076–1080 |
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CLIP@UMD at SemEval-2016 Task 8: Parser for Abstract Meaning Representation using Learning to Search Sudha Rao, Yogarshi Vyas, Hal Daumé III and Philip Resnik |
pp. 1081–1087 |
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CU-NLP at SemEval-2016 Task 8: AMR Parsing using LSTM-based Recurrent Neural Networks William Foland and James H. Martin |
pp. 1088–1092 |
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CMU at SemEval-2016 Task 8: Graph-based AMR Parsing with Infinite Ramp Loss Jeffrey Flanigan, Chris Dyer, Noah A. Smith and Jaime Carbonell |
pp. 1093–1097 |
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IHS-RD-Belarus at SemEval-2016 Task 9: Transition-based Chinese Semantic Dependency Parsing with Online Reordering and Bootstrapping. Artsiom Artsymenia, Palina Dounar and Maria Yermakovich |
pp. 1098–1102 |
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OCLSP at SemEval-2016 Task 9: Multilayered LSTM as a Neural Semantic Dependency Parser Lifeng Jin, Manjuan Duan and William Schuler |
pp. 1103–1108 |
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OSU_CHGCG at SemEval-2016 Task 9 : Chinese Semantic Dependency Parsing with Generalized Categorial Grammar Manjuan Duan, Lifeng Jin and William Schuler |
pp. 1109–1115 |
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LIMSI at SemEval-2016 Task 12: machine-learning and temporal information to identify clinical events and time expressions Cyril Grouin and Véronique MORICEAU |
pp. 1116–1121 |
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Hitachi at SemEval-2016 Task 12: A Hybrid Approach for Temporal Information Extraction from Clinical Notes Sarath P R, Manikandan R and Yoshiki Niwa |
pp. 1122–1127 |
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CDE-IIITH at SemEval-2016 Task 12: Extraction of Temporal Information from Clinical documents using Machine Learning techniques Veera Raghavendra Chikka |
pp. 1128–1131 |
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VUACLTL at SemEval 2016 Task 12: A CRF Pipeline to Clinical TempEval Tommaso Caselli and Roser Morante |
pp. 1132–1138 |
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GUIR at SemEval-2016 task 12: Temporal Information Processing for Clinical Narratives Arman Cohan, Kevin Meurer and Nazli Goharian |
pp. 1139–1146 |
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UtahBMI at SemEval-2016 Task 12: Extracting Temporal Information from Clinical Text Abdulrahman AAl Abdulsalam, Sumithra Velupillai and Stephane Meystre |
pp. 1147–1153 |
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ULISBOA at SemEval-2016 Task 12: Extraction of temporal expressions, clinical events and relations using IBEnt Marcia Barros, André Lamúrias, Gonçalo Figueiró, Marta Antunes, Joana Teixeira, Alexandre Pinheiro and Francisco M. Couto |
pp. 1154–1158 |
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UTA DLNLP at SemEval-2016 Task 12: Deep Learning Based Natural Language Processing System for Clinical Information Identification from Clinical Notes and Pathology Reports Peng Li and Heng Huang |
pp. 1159–1164 |
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Brundlefly at SemEval-2016 Task 12: Recurrent Neural Networks vs. Joint Inference for Clinical Temporal Information Extraction Jason Fries |
pp. 1165–1170 |
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KULeuven-LIIR at SemEval 2016 Task 12: Detecting Narrative Containment in Clinical Records Artuur Leeuwenberg and Marie-Francine Moens |
pp. 1171–1176 |
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CENTAL at SemEval-2016 Task 12: a linguistically fed CRF model for medical and temporal information extraction Charlotte Hansart, Damien De Meyere, Patrick Watrin, André Bittar and Cédrick Fairon |
pp. 1177–1182 |
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UTHealth at SemEval-2016 Task 12: an End-to-End System for Temporal Information Extraction from Clinical Notes Hee-Jin Lee, Hua Xu, Jingqi Wang, Yaoyun Zhang, Sungrim Moon, Jun Xu and Yonghui Wu |
pp. 1183–1188 |
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NUIG-UNLP at SemEval-2016 Task 13: A Simple Word Embedding-based Approach for Taxonomy Extraction Joel Pocostales |
pp. 1189–1193 |
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USAAR at SemEval-2016 Task 13: Hyponym Endocentricity Liling Tan, Francis Bond and Josef van Genabith |
pp. 1194–1200 |
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JUNLP at SemEval-2016 Task 13: A Language Independent Approach for Hypernym Identification Promita Maitra and Dipankar Das |
pp. 1201–1205 |
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QASSIT at SemEval-2016 Task 13: On the integration of Semantic Vectors in Pretopological Spaces for Lexical Taxonomy Acquisition Guillaume Cleuziou and Jose G. Moreno |
pp. 1206–1210 |
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TAXI at SemEval-2016 Task 13: a Taxonomy Induction Method based on Lexico-Syntactic Patterns, Substrings and Focused Crawling Alexander Panchenko, Stefano Faralli, Eugen Ruppert, Steffen Remus, Hubert Naets, Cedrick Fairon, Simone Paolo Ponzetto and Chris Biemann |
pp. 1211–1218 |
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Duluth at SemEval 2016 Task 14: Extending Gloss Overlaps to Enrich Semantic Taxonomies Ted Pedersen |
pp. 1219–1222 |
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TALN at SemEval-2016 Task 14: Semantic Taxonomy Enrichment Via Sense-Based Embeddings Luis Espinosa Anke, Francesco Ronzano and Horacio Saggion |
pp. 1223–1227 |
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MSejrKu at SemEval-2016 Task 14: Taxonomy Enrichment by Evidence Ranking Michael Schlichtkrull and Héctor Martínez Alonso |
pp. 1228–1232 |
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Deftor at SemEval-2016 Task 14: Taxonomy enrichment using definition vectors Hristo Tanev and Agata Rotondi |
pp. 1233–1236 |
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UMNDuluth at SemEval-2016 Task 14: WordNet’s Missing Lemmas Jon Rusert and Ted Pedersen |
pp. 1237–1241 |
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VCU at Semeval-2016 Task 14: Evaluating definitional-based similarity measure for semantic taxonomy enrichment Bridget McInnes |
pp. 1242–1246 |
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GWU NLP at SemEval-2016 Shared Task 1: Matrix Factorization for Crosslingual STS Hanan Aldarmaki and Mona Diab |
pp. 1247–1251 |
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CNRC at SemEval-2016 Task 1: Experiments in Crosslingual Semantic Textual Similarity Chi-kiu Lo, Cyril Goutte and Michel Simard |
pp. 1252–1257 |
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MayoNLP at SemEval-2016 Task 1: Semantic Textual Similarity based on Lexical Semantic Net and Deep Learning Semantic Model Naveed Afzal, Yanshan Wang and Hongfang Liu |
pp. 1258–1263 |
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UoB-UK at SemEval-2016 Task 1: A Flexible and Extendable System for Semantic Text Similarity using Types, Surprise and Phrase Linking Harish Tayyar Madabushi, Mark Buhagiar and Mark Lee |
pp. 1264–1269 |
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BIT at SemEval-2016 Task 1: Sentence Similarity Based on Alignments and Vector with the Weight of Information Content Hao Wu, Heyan Huang and Wenpeng Lu |
pp. 1270–1274 |
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RICOH at SemEval-2016 Task 1: IR-based Semantic Textual Similarity Estimation Hideo Itoh |
pp. 1275–1279 |
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IHS-RD-Belarus at SemEval-2016 Task 1: Multistage Approach for Measuring Semantic Similarity Maryna Beliuha and Maryna Chernyshevich |
pp. 1280–1285 |
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JUNITMZ at SemEval-2016 Task 1: Identifying Semantic Similarity Using Levenshtein Ratio Sandip Sarkar, Dipankar Das, Partha Pakray and Alexander Gelbukh |
pp. 1286–1289 |
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Amrita_CEN at SemEval-2016 Task 1: Semantic Relation from Word Embeddings in Higher Dimension Barathi Ganesh HB, Anand Kumar M and Soman KP |
pp. 1290–1295 |
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NUIG-UNLP at SemEval-2016 Task 1: Soft Alignment and Deep Learning for Semantic Textual Similarity John Philip McCrae, Kartik Asooja, Nitish Aggarwal and Paul Buitelaar |
pp. 1296–1301 |
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NORMAS at SemEval-2016 Task 1: SEMSIM: A Multi-Feature Approach to Semantic Text Similarity kolawole adebayo, Luigi Di Caro and Guido Boella |
pp. 1302–1309 |
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LIPN-IIMAS at SemEval-2016 Task 1: Random Forest Regression Experiments on Align-and-Differentiate and Word Embeddings penalizing strategies Oscar William Lightgow Serrano, Ivan Vladimir Meza Ruiz, Albert Manuel Orozco Camacho, Jorge Garcia Flores and Davide Buscaldi |
pp. 1310–1315 |
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UNBNLP at SemEval-2016 Task 1: Semantic Textual Similarity: A Unified Framework for Semantic Processing and Evaluation Milton King, Waseem Gharbieh, SoHyun Park and Paul Cook |
pp. 1316–1319 |
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ASOBEK at SemEval-2016 Task 1: Sentence Representation with Character N-gram Embeddings for Semantic Textual Similarity Asli Eyecioglu and Bill Keller |
pp. 1320–1324 |
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SimiHawk at SemEval-2016 Task 1: A Deep Ensemble System for Semantic Textual Similarity Peter Potash, William Boag, Alexey Romanov, Vasili Ramanishka and Anna Rumshisky |
pp. 1325–1332 |
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SERGIOJIMENEZ at SemEval-2016 Task 1: Effectively Combining Paraphrase Database, String Matching, WordNet, and Word Embedding for Semantic Textual Similarity Sergio Jimenez |
pp. 1333–1341 |
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RTM at SemEval-2016 Task 1: Predicting Semantic Similarity with Referential Translation Machines and Related Statistics Ergun Bicici |
pp. 1342–1348 |
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DalGTM at SemEval-2016 Task 1: Importance-Aware Compositional Approach to Short Text Similarity Jie Mei, Aminul Islam and Evangelos Milios |
pp. 1349–1354 |
Last modified on June 15, 2016, 8:44 a.m.