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Proceedings of the 10th International Workshop on Semantic Evaluation

Chairs
Steven Bethard, University of Alabama at Birmingham \\ Daniel Cer, Google \\ Marine Carpuat, University of Maryland \\ David Jurgens, Stanford University \\ Preslav Nakov, Qatar Computing Research Institute, HBKU \\ Torsten Zesch, University of Duisburg-Essen

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pdf bib Front matter pages
pdf bib SemEval-2016 Task 4: Sentiment Analysis in Twitter
Preslav Nakov, Alan Ritter, Sara Rosenthal, Fabrizio Sebastiani and Veselin Stoyanov
pp. 1–18
pdf bib 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
pdf bib SemEval-2016 Task 6: Detecting Stance in Tweets
Saif Mohammad, Svetlana Kiritchenko, Parinaz Sobhani, Xiaodan Zhu and Colin Cherry
pp. 31–41
pdf bib SemEval-2016 Task 7: Determining Sentiment Intensity of English and Arabic Phrases
Svetlana Kiritchenko, Saif Mohammad and Mohammad Salameh
pp. 42–51
pdf bib CUFE at SemEval-2016 Task 4: A Gated Recurrent Model for Sentiment Classification
Mahmoud Nabil, Amir Atyia and Mohamed Aly
pp. 52–57
pdf bib 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
pdf bib 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
pdf bib 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
pdf bib 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
pdf bib TwiSE at SemEval-2016 Task 4: Twitter Sentiment Classification
Georgios Balikas and Massih-Reza Amini
pp. 85–91
pdf bib ISTI-CNR at SemEval-2016 Task 4: Quantification on an Ordinal Scale
Andrea Esuli
pp. 92–95
pdf bib 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
pdf bib thecerealkiller at SemEval-2016 Task 4: Deep Learning based System for Classifying Sentiment of Tweets on Two Point Scale
Vikrant Yadav
pp. 100–102
pdf bib 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
pdf bib 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
pdf bib 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
pdf bib Aicyber at SemEval-2016 Task 4: i-vector based sentence representation
Steven Du and Xi Zhang
pp. 120–125
pdf bib 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
pdf bib PUT at SemEval-2016 Task 4: The ABC of Twitter Sentiment Analysis
Mateusz Lango, Dariusz Brzezinski and Jerzy Stefanowski
pp. 131–137
pdf bib mib at SemEval-2016 Task 4a: Exploiting lexicon based features for Sentiment Analysis in Twitter
Vittoria Cozza and Marinella Petrocchi
pp. 138–143
pdf bib MDSENT at SemEval-2016 Task 4: A Supervised System for Message Polarity Classification
Hang Gao and Tim Oates
pp. 144–149
pdf bib 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
pdf bib 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
pdf bib 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
pdf bib UofL at SemEval-2016 Task 4: Multi Domain word2vec for Twitter Sentiment Classification
Omar Abdelwahab and Adel Elmaghraby
pp. 169–175
pdf bib 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
pdf bib 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
pdf bib 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
pdf bib SentiSys at SemEval-2016 Task 4: Feature-Based System for Sentiment Analysis in Twitter
Hussam Hamdan
pp. 195–202
pdf bib 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
pdf bib SENSEI-LIF at SemEval-2016 Task 4: Polarity embedding fusion for robust sentiment analysis
Mickael Rouvier and Benoit Favre
pp. 207–213
pdf bib DiegoLab16 at SemEval-2016 Task 4: Sentiment Analysis in Twitter using Centroids, Clusters, and Sentiment Lexicons
Abeed Sarker and Graciela Gonzalez
pp. 214–219
pdf bib VCU-TSA at Semeval-2016 Task 4: Sentiment Analysis in Twitter
Gerard Briones, Kasun Amarasinghe and Bridget McInnes
pp. 220–224
pdf bib UniPI at SemEval-2016 Task 4: Convolutional Neural Networks for Sentiment Classification
Giuseppe Attardi and Daniele Sartiano
pp. 225–229
pdf bib IIP at SemEval-2016 Task 4: Prioritizing Classes in Ensemble Classification for Sentiment Analysis of Tweets
Jasper Friedrichs
pp. 230–234
pdf bib PotTS at SemEval-2016 Task 4: Sentiment Analysis of Twitter Using Character-level Convolutional Neural Networks.
Uladzimir Sidarenka
pp. 235–242
pdf bib 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
pdf bib 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
pdf bib 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
pdf bib 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
pdf bib 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
pdf bib OPAL at SemEval-2016 Task 4: the Challenge of Porting a Sentiment Analysis System to the "Real" World
Alexandra Balahur
pp. 267–270
pdf bib 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
pdf bib 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
pdf bib 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
pdf bib NLANGP at SemEval-2016 Task 5: Improving Aspect Based Sentiment Analysis using Neural Network Features
Zhiqiang Toh and Jian Su
pp. 287–293
pdf bib 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
pdf bib IHS-RD-Belarus at SemEval-2016 Task 5: Detecting Sentiment Polarity Using the Heatmap of Sentence
Maryna Chernyshevich
pp. 301–305
pdf bib BUTknot at SemEval-2016 Task 5: Supervised Machine Learning with Term Substitution Approach in Aspect Category Detection
Jakub Machacek
pp. 306–310
pdf bib 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
pdf bib 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
pdf bib 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
pdf bib AKTSKI at SemEval-2016 Task 5: Aspect Based Sentiment Analysis for Consumer Reviews
Shubham Pateria and Prafulla Choubey
pp. 330–336
pdf bib 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
pdf bib 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
pdf bib 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
pdf bib UWB at SemEval-2016 Task 5: Aspect Based Sentiment Analysis
Tomáš Hercig, Tomáš Brychcín, Lukáš Svoboda and Michal Konkol
pp. 354–361
pdf bib SentiSys at SemEval-2016 Task 5: Opinion Target Extraction and Sentiment Polarity Detection
Hussam Hamdan
pp. 362–367
pdf bib COMMIT at SemEval-2016 Task 5: Sentiment Analysis with Rhetorical Structure Theory
Kim Schouten and Flavius Frasincar
pp. 368–372
pdf bib 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
pdf bib UFAL at SemEval-2016 Task 5: Recurrent Neural Networks for Sentence Classification
Aleš Tamchyna and Kateřina Veselovská
pp. 379–383
pdf bib UWaterloo at SemEval-2016 Task 5: Minimally Supervised Approaches to Aspect-Based Sentiment Analysis
Olga Vechtomova and Anni He
pp. 384–389
pdf bib 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
pdf bib 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
pdf bib USFD at SemEval-2016 Task 6: Any-Target Stance Detection on Twitter with Autoencoders
Isabelle Augenstein, Andreas Vlachos and Kalina Bontcheva
pp. 401–405
pdf bib 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
pdf bib 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
pdf bib UWB at SemEval-2016 Task 6: Stance Detection
Peter Krejzl and Josef Steinberger
pp. 420–424
pdf bib 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
pdf bib 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
pdf bib ltl.uni-due at SemEval-2016 Task 6: Stance Detection in Social Media Using Stacked Classifiers
Michael Wojatzki and Torsten Zesch
pp. 440–445
pdf bib CU-GWU Perspective at SemEval-2016 Task 6: Ideological Stance Detection in Informal Text
Heba Elfardy and Mona Diab
pp. 446–451
pdf bib 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
pdf bib 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
pdf bib 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
pdf bib MITRE at SemEval-2016 Task 6: Transfer Learning for Stance Detection
Guido Zarrella and Amy Marsh
pp. 470–475
pdf bib 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
pdf bib 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
pdf bib 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
pdf bib UWB at SemEval-2016 Task 7: Novel Method for Automatic Sentiment Intensity Determination
Ladislav Lenc, Pavel Král and Václav Rajtmajer
pp. 493–497
pdf bib NileTMRG at SemEval-2016 Task 7: Deriving Prior Polarities for Arabic Sentiment Terms
Samhaa R. El-Beltagy
pp. 498–502
pdf bib 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
pdf bib 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
pdf bib 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
pdf bib 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
pdf bib SemEval-2016 Task 10: Detecting Minimal Semantic Units and their Meanings (DiMSUM)
Nathan Schneider, Dirk Hovy, Anders Johannsen and Marine Carpuat
pp. 558–571
pdf bib SemEval 2016 Task 11: Complex Word Identification
Gustavo Paetzold and Lucia Specia
pp. 572–581
pdf bib 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
pdf bib VRep at SemEval-2016 Task 1 and Task 2: A System for Interpretable Semantic Similarity
Sam Henry and Allison Sands
pp. 589–595
pdf bib 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
pdf bib UWB at SemEval-2016 Task 1: Semantic Textual Similarity using Lexical, Syntactic, and Semantic Information
Tomáš Brychcín and Lukáš Svoboda
pp. 600–606
pdf bib 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
pdf bib 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
pdf bib 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
pdf bib 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
pdf bib 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
pdf bib 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
pdf bib 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
pdf bib 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
pdf bib 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
pdf bib 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
pdf bib DLS@CU at SemEval-2016 Task 1: Supervised Models of Sentence Similarity
Md Arafat Sultan, Steven Bethard and Tamara Sumner
pp. 668–673
pdf bib DCU-SEManiacs at SemEval-2016 Task 1: Synthetic Paragram Embeddings for Semantic Textual Similarity
Chris Hokamp and Piyush Arora
pp. 674–680
pdf bib iUBC at SemEval-2016 Task 2: RNNs and LSTMs for interpretable STS
Inigo Lopez-Gazpio, Eneko Agirre and Montse Maritxalar
pp. 681–686
pdf bib 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
pdf bib Inspire at SemEval-2016 Task 2: Interpretable Semantic Textual Similarity Alignment based on Answer Set Programming
Mishal Kazmi and Peter Schüller
pp. 693–699
pdf bib 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
pdf bib IISCNLP at SemEval-2016 Task 2: Interpretable STS with ILP based Multiple Chunk Aligner
Lavanya Tekumalla and Sharmistha Jat
pp. 707–712
pdf bib VENSESEVAL at Semeval-2016 Task 2 iSTS - with a full-fledged rule-based approach
Rodolfo Delmonte
pp. 713–719
pdf bib 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
pdf bib 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
pdf bib 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
pdf bib 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
pdf bib 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
pdf bib 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
pdf bib 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
pdf bib 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
pdf bib 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
pdf bib ICL00 at SemEval-2016 Task 3: Translation-Based Method for CQA System
Yunfang Wu and Minghua Zhang
pp. 782–785
pdf bib 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
pdf bib 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
pdf bib 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
pdf bib 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
pdf bib 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
pdf bib 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
pdf bib ITNLP-AiKF at SemEval-2016 Task 3 a quesiton answering system using community QA repository
Chang e Jia
pp. 829–834
pdf bib 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
pdf bib 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
pdf bib UTU at SemEval-2016 Task 10: Binary Classification for Expression Detection (BCED)
Jari Björne and Tapio Salakoski
pp. 850–855
pdf bib 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
pdf bib 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
pdf bib 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
pdf bib PLUJAGH at SemEval-2016 Task 11: Simple System for Complex Word Identification
Krzysztof Wróbel
pp. 878–882
pdf bib 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
pdf bib Sensible at SemEval-2016 Task 11: Neural Nonsense Mangled in Ensemble Mess
Gillin Nat
pp. 888–893
pdf bib SV000gg at SemEval-2016 Task 11: Heavy Gauge Complex Word Identification with System Voting
Gustavo Paetzold and Lucia Specia
pp. 894–899
pdf bib 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
pdf bib CLaC at SemEval-2016 Task 11: Exploring linguistic and psycho-linguistic Features for Complex Word Identification
Elnaz Davoodi and Leila Kosseim
pp. 907–910
pdf bib 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
pdf bib MAZA at SemEval-2016 Task 11: Detecting Lexical Complexity Using a Decision Stump Meta-Classifier
Shervin Malmasi and Marcos Zampieri
pp. 916–920
pdf bib LTG at SemEval-2016 Task 11: Complex Word Identification with Classifier Ensembles
Shervin Malmasi, Mark Dras and Marcos Zampieri
pp. 921–925
pdf bib MacSaar at SemEval-2016 Task 11: Zipfian and Character Features for ComplexWord Identification
Marcos Zampieri, Liling Tan and Josef van Genabith
pp. 926–930
pdf bib Garuda & Bhasha at SemEval-2016 Task 11: Complex Word Identification Using Aggregated Learning Models
Prafulla Choubey and Shubham Pateria
pp. 931–935
pdf bib 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
pdf bib IIIT at SemEval-2016 Task 11: Complex Word Identification using Nearest Centroid Classification
Ashish Palakurthi and Radhika Mamidi
pp. 942–946
pdf bib AmritaCEN at SemEval-2016 Task 11: Complex Word Identification using Word Embedding
sanjay sp, Anand Kumar and Soman K P
pp. 947–952
pdf bib 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
pdf bib HMC at SemEval-2016 Task 11: Identifying Complex Words Using Depth-limited Decision Trees
Maury Quijada and Julie Medero
pp. 959–962
pdf bib UWB at SemEval-2016 Task 11: Exploring Features for Complex Word Identification
Michal Konkol
pp. 963–966
pdf bib AI-KU at SemEval-2016 Task 11: Word Embeddings and Substring Features for Complex Word Identification
Onur Kuru
pp. 967–971
pdf bib Pomona at SemEval-2016 Task 11: Predicting Word Complexity Based on Corpus Frequency
David Kauchak
pp. 972–976
pdf bib SemEval-2016 Task 12: Clinical TempEval
Steven Bethard, Guergana Savova, Wei-Te Chen, Leon Derczynski, James Pustejovsky and Marc Verhagen
pp. 977–987
pdf bib SemEval-2016 Task 8: Meaning Representation Parsing
Jonathan May
pp. 988–997
pdf bib SemEval-2016 Task 9: Chinese Semantic Dependency Parsing
Wanxiang Che, Yanqiu Shao, Ting Liu and Yu Ding
pp. 998–1004
pdf bib SemEval-2016 Task 13: Taxonomy Extraction Evaluation (TExEval-2)
Georgeta Bordea, Els Lefever and Paul Buitelaar
pp. 1005–1015
pdf bib SemEval-2016 Task 14: Semantic Taxonomy Enrichment
David Jurgens and Mohammad Taher Pilehvar
pp. 1016–1026
pdf bib 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
pdf bib 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
pdf bib DynamicPower at SemEval-2016 Task 8: Processing syntactic parse trees with a Dynamic Semantics core
Alastair Butler
pp. 1039–1044
pdf bib M2L at SemEval-2016 Task 8: AMR Parsing with Neural Networks
Yevgeniy Puzikov, Daisuke Kawahara and Sadao Kurohashi
pp. 1045–1050
pdf bib 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
pdf bib 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
pdf bib 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
pdf bib The Meaning Factory at SemEval-2016 Task 8: Producing AMRs with Boxer
Johannes Bjerva, Johan Bos and Hessel Haagsma
pp. 1070–1075
pdf bib UofR at SemEval-2016 Task 8: Learning Synchronous Hyperedge Replacement Grammar for AMR Parsing
Xiaochang Peng and Daniel Gildea
pp. 1076–1080
pdf bib 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
pdf bib CU-NLP at SemEval-2016 Task 8: AMR Parsing using LSTM-based Recurrent Neural Networks
William Foland and James H. Martin
pp. 1088–1092
pdf bib 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
pdf bib 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
pdf bib OCLSP at SemEval-2016 Task 9: Multilayered LSTM as a Neural Semantic Dependency Parser
Lifeng Jin, Manjuan Duan and William Schuler
pp. 1103–1108
pdf bib 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
pdf bib 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
pdf bib 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
pdf bib CDE-IIITH at SemEval-2016 Task 12: Extraction of Temporal Information from Clinical documents using Machine Learning techniques
Veera Raghavendra Chikka
pp. 1128–1131
pdf bib VUACLTL at SemEval 2016 Task 12: A CRF Pipeline to Clinical TempEval
Tommaso Caselli and Roser Morante
pp. 1132–1138
pdf bib GUIR at SemEval-2016 task 12: Temporal Information Processing for Clinical Narratives
Arman Cohan, Kevin Meurer and Nazli Goharian
pp. 1139–1146
pdf bib UtahBMI at SemEval-2016 Task 12: Extracting Temporal Information from Clinical Text
Abdulrahman AAl Abdulsalam, Sumithra Velupillai and Stephane Meystre
pp. 1147–1153
pdf bib 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
pdf bib 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
pdf bib Brundlefly at SemEval-2016 Task 12: Recurrent Neural Networks vs. Joint Inference for Clinical Temporal Information Extraction
Jason Fries
pp. 1165–1170
pdf bib KULeuven-LIIR at SemEval 2016 Task 12: Detecting Narrative Containment in Clinical Records
Artuur Leeuwenberg and Marie-Francine Moens
pp. 1171–1176
pdf bib 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
pdf bib 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
pdf bib NUIG-UNLP at SemEval-2016 Task 13: A Simple Word Embedding-based Approach for Taxonomy Extraction
Joel Pocostales
pp. 1189–1193
pdf bib USAAR at SemEval-2016 Task 13: Hyponym Endocentricity
Liling Tan, Francis Bond and Josef van Genabith
pp. 1194–1200
pdf bib JUNLP at SemEval-2016 Task 13: A Language Independent Approach for Hypernym Identification
Promita Maitra and Dipankar Das
pp. 1201–1205
pdf bib 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
pdf bib 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
pdf bib Duluth at SemEval 2016 Task 14: Extending Gloss Overlaps to Enrich Semantic Taxonomies
Ted Pedersen
pp. 1219–1222
pdf bib TALN at SemEval-2016 Task 14: Semantic Taxonomy Enrichment Via Sense-Based Embeddings
Luis Espinosa Anke, Francesco Ronzano and Horacio Saggion
pp. 1223–1227
pdf bib MSejrKu at SemEval-2016 Task 14: Taxonomy Enrichment by Evidence Ranking
Michael Schlichtkrull and Héctor Martínez Alonso
pp. 1228–1232
pdf bib Deftor at SemEval-2016 Task 14: Taxonomy enrichment using definition vectors
Hristo Tanev and Agata Rotondi
pp. 1233–1236
pdf bib UMNDuluth at SemEval-2016 Task 14: WordNet’s Missing Lemmas
Jon Rusert and Ted Pedersen
pp. 1237–1241
pdf bib VCU at Semeval-2016 Task 14: Evaluating definitional-based similarity measure for semantic taxonomy enrichment
Bridget McInnes
pp. 1242–1246
pdf bib GWU NLP at SemEval-2016 Shared Task 1: Matrix Factorization for Crosslingual STS
Hanan Aldarmaki and Mona Diab
pp. 1247–1251
pdf bib CNRC at SemEval-2016 Task 1: Experiments in Crosslingual Semantic Textual Similarity
Chi-kiu Lo, Cyril Goutte and Michel Simard
pp. 1252–1257
pdf bib 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
pdf bib 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
pdf bib 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
pdf bib RICOH at SemEval-2016 Task 1: IR-based Semantic Textual Similarity Estimation
Hideo Itoh
pp. 1275–1279
pdf bib IHS-RD-Belarus at SemEval-2016 Task 1: Multistage Approach for Measuring Semantic Similarity
Maryna Beliuha and Maryna Chernyshevich
pp. 1280–1285
pdf bib JUNITMZ at SemEval-2016 Task 1: Identifying Semantic Similarity Using Levenshtein Ratio
Sandip Sarkar, Dipankar Das, Partha Pakray and Alexander Gelbukh
pp. 1286–1289
pdf bib 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
pdf bib 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
pdf bib 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
pdf bib 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
pdf bib 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
pdf bib 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
pdf bib 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
pdf bib 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
pdf bib RTM at SemEval-2016 Task 1: Predicting Semantic Similarity with Referential Translation Machines and Related Statistics
Ergun Bicici
pp. 1342–1348
pdf bib 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.