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SemEval@NAACL-HLT 2016: San Diego, California, USA
- Steven Bethard, Daniel M. Cer, Marine Carpuat, David Jurgens, Preslav Nakov, Torsten Zesch:
Proceedings of the 10th International Workshop on Semantic Evaluation, SemEval@NAACL-HLT 2016, San Diego, CA, USA, June 16-17, 2016. The Association for Computer Linguistics 2016, ISBN 978-1-941643-95-2 - Preslav Nakov, Alan Ritter, Sara Rosenthal, Fabrizio Sebastiani, Veselin Stoyanov:
SemEval-2016 Task 4: Sentiment Analysis in Twitter. 1-18 - Maria Pontiki
, Dimitris Galanis, Haris Papageorgiou, Ion Androutsopoulos
SemEval-2016 Task 5: Aspect Based Sentiment Analysis. 19-30 - Saif M. Mohammad, Svetlana Kiritchenko, Parinaz Sobhani, Xiaodan Zhu, Colin Cherry:
SemEval-2016 Task 6: Detecting Stance in Tweets. 31-41 - Svetlana Kiritchenko, Saif M. Mohammad, Mohammad Salameh:
SemEval-2016 Task 7: Determining Sentiment Intensity of English and Arabic Phrases. 42-51 - Mahmoud Nabil, Amir F. Atiya
CUFE at SemEval-2016 Task 4: A Gated Recurrent Model for Sentiment Classification. 52-57 - Giovanni Da San Martino, Wei Gao, Fabrizio Sebastiani:
QCRI at SemEval-2016 Task 4: Probabilistic Methods for Binary and Ordinal Quantification. 58-63 - Stefan Räbiger, Mishal Kazmi, Yücel Saygin, Peter Schüller, Myra Spiliopoulou:
SteM at SemEval-2016 Task 4: Applying Active Learning to Improve Sentiment Classification. 64-70 - Zhengchen Zhang, Chen Zhang, Fuxiang Wu, Dong-Yan Huang, Weisi Lin, Minghui Dong:
I2RNTU at SemEval-2016 Task 4: Classifier Fusion for Polarity Classification in Twitter. 71-78 - David Vilares
LyS at SemEval-2016 Task 4: Exploiting Neural Activation Values for Twitter Sentiment Classification and Quantification. 79-84 - Georgios Balikas, Massih-Reza Amini:
TwiSE at SemEval-2016 Task 4: Twitter Sentiment Classification. 85-91 - Andrea Esuli
ISTI-CNR at SemEval-2016 Task 4: Quantification on an Ordinal Scale. 92-95 - Stavros Giorgis, Apostolos Rousas, John Pavlopoulos
aueb.twitter.sentiment at SemEval-2016 Task 4: A Weighted Ensemble of SVMs for Twitter Sentiment Analysis. 96-99 - Vikrant Yadav:
thecerealkiller at SemEval-2016 Task 4: Deep Learning based System for Classifying Sentiment of Tweets on Two Point Scale. 100-102 - Brage Ekroll Jahren, Valerij Fredriksen, Björn Gambäck
NTNUSentEval at SemEval-2016 Task 4: Combining General Classifiers for Fast Twitter Sentiment Analysis. 103-108 - Esteban Castillo, Ofelia Cervantes, Darnes Vilariño, David Báez:
UDLAP at SemEval-2016 Task 4: Sentiment Quantification Using a Graph Based Representation. 109-114 - Jonathan Juncal-Martínez, Tamara Álvarez-López, Milagros Fernández Gavilanes, Enrique Costa-Montenegro, Francisco Javier González-Castaño:
GTI at SemEval-2016 Task 4: Training a Naive Bayes Classifier using Features of an Unsupervised System. 115-119 - Steven Du, Xi Zhang:
Aicyber at SemEval-2016 Task 4: i-vector based sentence representation. 120-125 - Mateusz Lango
PUT at SemEval-2016 Task 4: The ABC of Twitter Sentiment Analysis. 126-132 - Vittoria Cozza, Marinella Petrocchi:
mib at SemEval-2016 Task 4a: Exploiting lexicon based features for Sentiment Analysis in Twitter. 133-138 - Hang Gao, Tim Oates:
MDSENT at SemEval-2016 Task 4: A Supervised System for Message Polarity Classification. 139-144 - Helena Gómez-Adorno, Darnes Vilariño, Grigori Sidorov, David Pinto Avendaño:
CICBUAPnlp at SemEval-2016 Task 4-A: Discovering Twitter Polarity using Enhanced Embeddings. 145-148 - Dario Stojanovski, Gjorgji Strezoski, Gjorgji Madjarov, Ivica Dimitrovski
Finki at SemEval-2016 Task 4: Deep Learning Architecture for Twitter Sentiment Analysis. 149-154 - Elisavet Palogiannidi, Athanasia Kolovou, Fenia Christopoulou, Filippos Kokkinos, Elias Iosif, Nikolaos Malandrakis, Haris Papageorgiou, Shrikanth S. Narayanan, Alexandros Potamianos:
Tweester at SemEval-2016 Task 4: Sentiment Analysis in Twitter Using Semantic-Affective Model Adaptation. 155-163 - Omar Abdelwahab, Adel Elmaghraby
UofL at SemEval-2016 Task 4: Multi Domain word2vec for Twitter Sentiment Classification. 164-170 - Nikolay Karpov, Alexander Porshnev, Kirill Rudakov:
NRU-HSE at SemEval-2016 Task 4: Comparative Analysis of Two Iterative Methods Using Quantification Library. 171-177 - Sebastian Ruder, Parsa Ghaffari, John G. Breslin:
INSIGHT-1 at SemEval-2016 Task 4: Convolutional Neural Networks for Sentiment Classification and Quantification. 178-182 - Steven Xu, Huizhi Liang, Timothy Baldwin:
UNIMELB at SemEval-2016 Tasks 4A and 4B: An Ensemble of Neural Networks and a Word2Vec Based Model for Sentiment Classification. 183-189 - Hussam Hamdan:
SentiSys at SemEval-2016 Task 4: Feature-Based System for Sentiment Analysis in Twitter. 190-197 - Victor Martinez Morant, Lluís-F. Hurtado, Ferran Pla:
DSIC-ELIRF at SemEval-2016 Task 4: Message Polarity Classification in Twitter using a Support Vector Machine Approach. 198-201 - Mickael Rouvier, Benoît Favre:
SENSEI-LIF at SemEval-2016 Task 4: Polarity embedding fusion for robust sentiment analysis. 202-208 - Abeed Sarker
DiegoLab16 at SemEval-2016 Task 4: Sentiment Analysis in Twitter using Centroids, Clusters, and Sentiment Lexicons. 209-214 - Gerard Briones, Kasun Amarasinghe, Bridget T. McInnes:
VCU-TSA at Semeval-2016 Task 4: Sentiment Analysis in Twitter. 215-219 - Giuseppe Attardi, Daniele Sartiano
UniPI at SemEval-2016 Task 4: Convolutional Neural Networks for Sentiment Classification. 220-224 - Jasper Friedrichs:
IIP at SemEval-2016 Task 4: Prioritizing Classes in Ensemble Classification for Sentiment Analysis of Tweets. 225-229 - Uladzimir Sidarenka:
PotTS at SemEval-2016 Task 4: Sentiment Analysis of Twitter Using Character-level Convolutional Neural Networks. 230-237 - Silvio Amir, Ramón Fernandez Astudillo, Wang Ling, Mário J. Silva, Isabel Trancoso:
INESC-ID at SemEval-2016 Task 4-A: Reducing the Problem of Out-of-Embedding Words. 238-242 - Cosmin Florean, Oana Bejenaru, Eduard Apostol, Octavian Ciobanu, Adrian Iftene, Diana Trandabat:
SentimentalITsts at SemEval-2016 Task 4: building a Twitter sentiment analyzer in your backyard. 243-246 - Calin-Cristian Ciubotariu, Marius-Valentin Hrisca, Mihail Gliga, Diana Darabana, Diana Trandabat, Adrian Iftene:
Minions at SemEval-2016 Task 4: or how to build a sentiment analyzer using off-the-shelf resources? 247-250 - Yunchao He, Liang-Chih Yu, Chin-Sheng Yang, K. Robert Lai, Weiyi Liu:
YZU-NLP Team at SemEval-2016 Task 4: Ordinal Sentiment Classification Using a Recurrent Convolutional Network. 251-255 - Yunxiao Zhou, Zhihua Zhang, Man Lan:
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. 256-261 - Alexandra Balahur
OPAL at SemEval-2016 Task 4: the Challenge of Porting a Sentiment Analysis System to the "Real" World. 262-265 - Stefan Falk, Andi Rexha, Roman Kern
Know-Center at SemEval-2016 Task 5: Using Word Vectors with Typed Dependencies for Opinion Target Expression Extraction. 266-270 - Talaat Khalil, Samhaa R. El-Beltagy:
NileTMRG at SemEval-2016 Task 5: Deep Convolutional Neural Networks for Aspect Category and Sentiment Extraction. 271-276 - Caroline Brun, Julien Perez, Claude Roux:
XRCE at SemEval-2016 Task 5: Feedbacked Ensemble Modeling on Syntactico-Semantic Knowledge for Aspect Based Sentiment Analysis. 277-281 - Zhiqiang Toh, Jian Su
NLANGP at SemEval-2016 Task 5: Improving Aspect Based Sentiment Analysis using Neural Network Features. 282-288 - Toshihiko Yanase, Kohsuke Yanai, Misa Sato, Toshinori Miyoshi, Yoshiki Niwa:
bunji at SemEval-2016 Task 5: Neural and Syntactic Models of Entity-Attribute Relationship for Aspect-based Sentiment Analysis. 289-295 - Maryna Chernyshevich:
IHS-RD-Belarus at SemEval-2016 Task 5: Detecting Sentiment Polarity Using the Heatmap of Sentence. 296-300 - Jakub Machacek:
BUTknot at SemEval-2016 Task 5: Supervised Machine Learning with Term Substitution Approach in Aspect Category Detection. 301-305 - Tamara Álvarez-López, Jonathan Juncal-Martínez, Milagros Fernández Gavilanes, Enrique Costa-Montenegro, Francisco Javier González-Castaño:
GTI at SemEval-2016 Task 5: SVM and CRF for Aspect Detection and Unsupervised Aspect-Based Sentiment Analysis. 306-311 - Dionysios Xenos, Panagiotis Theodorakakos, John Pavlopoulos
AUEB-ABSA at SemEval-2016 Task 5: Ensembles of Classifiers and Embeddings for Aspect Based Sentiment Analysis. 312-317 - Shubham Pateria, Prafulla Choubey:
AKTSKI at SemEval-2016 Task 5: Aspect Based Sentiment Analysis for Consumer Reviews. 318-324 - Vladimir Mayorov, Ivan Andrianov:
MayAnd at SemEval-2016 Task 5: Syntactic and word2vec-based approach to aspect-based polarity detection in Russian. 325-329 - Sebastian Ruder, Parsa Ghaffari, John G. Breslin:
INSIGHT-1 at SemEval-2016 Task 5: Deep Learning for Multilingual Aspect-based Sentiment Analysis. 330-336 - Fatih Samet Çetin, Ezgi Yildirim, Can Özbey, Gülsen Eryigit
TGB at SemEval-2016 Task 5: Multi-Lingual Constraint System for Aspect Based Sentiment Analysis. 337-341 - Tomás Hercig, Tomás Brychcín, Lukás Svoboda, Michal Konkol:
UWB at SemEval-2016 Task 5: Aspect Based Sentiment Analysis. 342-349 - Hussam Hamdan:
SentiSys at SemEval-2016 Task 5: Opinion Target Extraction and Sentiment Polarity Detection. 350-355 - Kim Schouten, Flavius Frasincar:
COMMIT at SemEval-2016 Task 5: Sentiment Analysis with Rhetorical Structure Theory. 356-360 - Mengxiao Jiang, Zhihua Zhang, Man Lan:
ECNU at SemEval-2016 Task 5: Extracting Effective Features from Relevant Fragments in Sentence for Aspect-Based Sentiment Analysis in Reviews. 361-366 - Ales Tamchyna, Katerina Veselovská:
UFAL at SemEval-2016 Task 5: Recurrent Neural Networks for Sentence Classification. 367-371 - Olga Vechtomova
UWaterloo at SemEval-2016 Task 5: Minimally Supervised Approaches to Aspect-Based Sentiment Analysis. 372-377 - Marcelo Dias, Karin Becker:
INF-UFRGS-OPINION-MINING at SemEval-2016 Task 6: Automatic Generation of a Training Corpus for Unsupervised Identification of Stance in Tweets. 378-383 - Wan Wei, Xiao Zhang, Xuqin Liu, Wei Chen, Tengjiao Wang:
pkudblab at SemEval-2016 Task 6 : A Specific Convolutional Neural Network System for Effective Stance Detection. 384-388 - Isabelle Augenstein, Andreas Vlachos
USFD at SemEval-2016 Task 6: Any-Target Stance Detection on Twitter with Autoencoders. 389-393 - Can Liu, Wen Li, Bradford Demarest, Yue Chen, Sara Couture, Daniel Dakota, Nikita Haduong, Noah Kaufman, Andrew Lamont, Manan Pancholi, Kenneth Steimel, Sandra Kübler:
IUCL at SemEval-2016 Task 6: An Ensemble Model for Stance Detection in Twitter. 394-400 - Yuki Igarashi, Hiroya Komatsu, Sosuke Kobayashi, Naoaki Okazaki, Kentaro Inui:
Tohoku at SemEval-2016 Task 6: Feature-based Model versus Convolutional Neural Network for Stance Detection. 401-407 - Peter Krejzl, Josef Steinberger:
UWB at SemEval-2016 Task 6: Stance Detection. 408-412 - Prashanth Vijayaraghavan, Ivan Sysoev, Soroush Vosoughi, Deb Roy:
DeepStance at SemEval-2016 Task 6: Detecting Stance in Tweets Using Character and Word-Level CNNs. 413-419 - Amita Misra
NLDS-UCSC at SemEval-2016 Task 6: A Semi-Supervised Approach to Detecting Stance in Tweets. 420-427 - Michael Wojatzki, Torsten Zesch:
ltl.uni-due at SemEval-2016 Task 6: Stance Detection in Social Media Using Stacked Classifiers. 428-433 - Heba Elfardy, Mona T. Diab:
CU-GWU Perspective at SemEval-2016 Task 6: Ideological Stance Detection in Informal Text. 434-439 - Braja Gopal Patra
JU_NLP at SemEval-2016 Task 6: Detecting Stance in Tweets using Support Vector Machines. 440-444 - Henrik Bøhler, Petter Asla, Erwin Marsi, Rune Sætre:
IDI$@$NTNU at SemEval-2016 Task 6: Detecting Stance in Tweets Using Shallow Features and GloVe Vectors for Word Representation. 445-450 - Zhihua Zhang, Man Lan:
ECNU at SemEval 2016 Task 6: Relevant or Not? Supportive or Not? A Two-step Learning System for Automatic Detecting Stance in Tweets. 451-457 - Guido Zarrella, Amy Marsh:
MITRE at SemEval-2016 Task 6: Transfer Learning for Stance Detection. 458-463 - Martin Tutek, Ivan Sekulic, Paula Gombar, Ivan Paljak, Filip Culinovic, Filip Boltuzic, Mladen Karan, Domagoj Alagic, Jan Snajder:
TakeLab at SemEval-2016 Task 6: Stance Classification in Tweets Using a Genetic Algorithm Based Ensemble. 464-468 - Amal Htait, Sébastien Fournier, Patrice Bellot:
LSIS at SemEval-2016 Task 7: Using Web Search Engines for English and Arabic Unsupervised Sentiment Intensity Prediction. 469-473 - Eshrag Refaee
iLab-Edinburgh at SemEval-2016 Task 7: A Hybrid Approach for Determining Sentiment Intensity of Arabic Twitter Phrases. 474-480 - Ladislav Lenc, Pavel Král, Václav Rajtmajer:
UWB at SemEval-2016 Task 7: Novel Method for Automatic Sentiment Intensity Determination. 481-485 - Samhaa R. El-Beltagy:
NileTMRG at SemEval-2016 Task 7: Deriving Prior Polarities for Arabic Sentiment Terms. 486-490 - Feixiang Wang, Zhihua Zhang, Man Lan:
ECNU at SemEval-2016 Task 7: An Enhanced Supervised Learning Method for Lexicon Sentiment Intensity Ranking. 491-496 - Eneko Agirre, Carmen Banea, Daniel M. Cer, Mona T. Diab, Aitor Gonzalez-Agirre, Rada Mihalcea, German Rigau
SemEval-2016 Task 1: Semantic Textual Similarity, Monolingual and Cross-Lingual Evaluation. 497-511 - Eneko Agirre, Aitor Gonzalez-Agirre, Iñigo Lopez-Gazpio, Montse Maritxalar
SemEval-2016 Task 2: Interpretable Semantic Textual Similarity. 512-524 - Preslav Nakov, Lluís Màrquez, Alessandro Moschitti, Walid Magdy
SemEval-2016 Task 3: Community Question Answering. 525-545 - Nathan Schneider, Dirk Hovy
SemEval-2016 Task 10: Detecting Minimal Semantic Units and their Meanings (DiMSUM). 546-559 - Gustavo Paetzold, Lucia Specia:
SemEval 2016 Task 11: Complex Word Identification. 560-569 - Duygu Ataman, José Guilherme Camargo de Souza
FBK HLT-MT at SemEval-2016 Task 1: Cross-lingual Semantic Similarity Measurement Using Quality Estimation Features and Compositional Bilingual Word Embeddings. 570-576 - Sam Henry, Allison Sands:
VRep at SemEval-2016 Task 1 and Task 2: A System for Interpretable Semantic Similarity. 577-583 - Peng Li, Heng Huang:
UTA DLNLP at SemEval-2016 Task 1: Semantic Textual Similarity: A Unified Framework for Semantic Processing and Evaluation. 584-587 - Tomás Brychcín, Lukás Svoboda:
UWB at SemEval-2016 Task 1: Semantic Textual Similarity using Lexical, Syntactic, and Semantic Information. 588-594 - Matthias Liebeck, Philipp Pollack, Pashutan Modaresi, Stefan Conrad:
HHU at SemEval-2016 Task 1: Multiple Approaches to Measuring Semantic Textual Similarity. 595-601 - Barbara Rychalska, Katarzyna Pakulska, Krystyna Chodorowska, Wojciech Walczak, Piotr Andruszkiewicz:
Samsung Poland NLP Team at SemEval-2016 Task 1: Necessity for diversity; combining recursive autoencoders, WordNet and ensemble methods to measure semantic similarity. 602-608 - Ahmet Aker, Frédéric Blain
USFD at SemEval-2016 Task 1: Putting different State-of-the-Arts into a Box. 609-613 - Piotr Przybyla, Nhung T. H. Nguyen, Matthew Shardlow, Georgios Kontonatsios
NaCTeM at SemEval-2016 Task 1: Inferring sentence-level semantic similarity from an ensemble of complementary lexical and sentence-level features. 614-620 - Junfeng Tian, Man Lan:
ECNU at SemEval-2016 Task 1: Leveraging Word Embedding From Macro and Micro Views to Boost Performance for Semantic Textual Similarity. 621-627 - Liling Tan, Carolina Scarton, Lucia Specia, Josef van Genabith:
SAARSHEFF at SemEval-2016 Task 1: Semantic Textual Similarity with Machine Translation Evaluation Metrics and (eXtreme) Boosted Tree Ensembles. 628-633 - Hanna Béchara, Rohit Gupta, Liling Tan, Constantin Orasan, Ruslan Mitkov, Josef van Genabith:
WOLVESAAR at SemEval-2016 Task 1: Replicating the Success of Monolingual Word Alignment and Neural Embeddings for Semantic Textual Similarity. 634-639 - Rajendra Banjade, Nabin Maharjan, Dipesh Gautam, Vasile Rus:
DTSim at SemEval-2016 Task 1: Semantic Similarity Model Including Multi-Level Alignment and Vector-Based Compositional Semantics. 640-644 - Cheng Fu, Bo An, Xianpei Han, Le Sun:
ISCAS_NLP at SemEval-2016 Task 1: Sentence Similarity Based on Support Vector Regression using Multiple Features. 645-649 - Md. Arafat Sultan, Steven Bethard, Tamara Sumner:
DLS$@$CU at SemEval-2016 Task 1: Supervised Models of Sentence Similarity. 650-655 - Chris Hokamp, Piyush Arora:
DCU-SEManiacs at SemEval-2016 Task 1: Synthetic Paragram Embeddings for Semantic Textual Similarity. 656-662 - Hanan Aldarmaki, Mona T. Diab:
GWU NLP at SemEval-2016 Shared Task 1: Matrix Factorization for Crosslingual STS. 663-667 - Chi-kiu Lo
CNRC at SemEval-2016 Task 1: Experiments in Crosslingual Semantic Textual Similarity. 668-673 - Naveed Afzal, Yanshan Wang
MayoNLP at SemEval-2016 Task 1: Semantic Textual Similarity based on Lexical Semantic Net and Deep Learning Semantic Model. 674-679 - Harish Tayyar Madabushi
UoB-UK at SemEval-2016 Task 1: A Flexible and Extendable System for Semantic Text Similarity using Types, Surprise and Phrase Linking. 680-685 - Hao Wu, Heyan Huang, Wenpeng Lu
BIT at SemEval-2016 Task 1: Sentence Similarity Based on Alignments and Vector with the Weight of Information Content. 686-690 - Hideo Itoh:
RICOH at SemEval-2016 Task 1: IR-based Semantic Textual Similarity Estimation. 691-695 - Maryna Beliuha, Maryna Chernyshevich:
IHS-RD-Belarus at SemEval-2016 Task 1: Multistage Approach for Measuring Semantic Similarity. 696-701 - Sandip Sarkar, Dipankar Das, Partha Pakray, Alexander F. Gelbukh
JUNITMZ at SemEval-2016 Task 1: Identifying Semantic Similarity Using Levenshtein Ratio. 702-705 - Barathi Ganesh H. B., M. Anand Kumar
Amrita_CEN at SemEval-2016 Task 1: Semantic Relation from Word Embeddings in Higher Dimension. 706-711 - John Philip McCrae, Kartik Asooja, Nitish Aggarwal, Paul Buitelaar:
NUIG-UNLP at SemEval-2016 Task 1: Soft Alignment and Deep Learning for Semantic Textual Similarity. 712-717 - Kolawole Adebayo, Luigi Di Caro, Guido Boella:
NORMAS at SemEval-2016 Task 1: SEMSIM: A Multi-Feature Approach to Semantic Text Similarity. 718-725 - Oscar William Lightgow Serrano, Iván Vladimir Meza Ruíz
LIPN-IIMAS at SemEval-2016 Task 1: Random Forest Regression Experiments on Align-and-Differentiate and Word Embeddings penalizing strategies. 726-731 - Milton King, Waseem Gharbieh, SoHyun Park, Paul Cook:
UNBNLP at SemEval-2016 Task 1: Semantic Textual Similarity: A Unified Framework for Semantic Processing and Evaluation. 732-735 - Asli Eyecioglu, Bill Keller
ASOBEK at SemEval-2016 Task 1: Sentence Representation with Character N-gram Embeddings for Semantic Textual Similarity. 736-740 - Peter Potash, William Boag, Alexey Romanov, Vasili Ramanishka, Anna Rumshisky:
SimiHawk at SemEval-2016 Task 1: A Deep Ensemble System for Semantic Textual Similarity. 741-748 - Sergio Jimenez:
SERGIOJIMENEZ at SemEval-2016 Task 1: Effectively Combining Paraphrase Database, String Matching, WordNet, and Word Embedding for Semantic Textual Similarity. 749-757 - Ergun Biçici:
RTM at SemEval-2016 Task 1: Predicting Semantic Similarity with Referential Translation Machines and Related Statistics. - Jie Mei, Aminul Islam, Evangelos E. Milios:
DalGTM at SemEval-2016 Task 1: Importance-Aware Compositional Approach to Short Text Similarity. 765-770 - Iñigo Lopez-Gazpio, Eneko Agirre
iUBC at SemEval-2016 Task 2: RNNs and LSTMs for interpretable STS. 771-776 - Ping Tan, Karin Verspoor
Rev at SemEval-2016 Task 2: Aligning Chunks by Lexical, Part of Speech and Semantic Equivalence. 777-782 - Simone Magnolini, Anna Feltracco, Bernardo Magnini:
FBK-HLT-NLP at SemEval-2016 Task 2: A Multitask, Deep Learning Approach for Interpretable Semantic Textual Similarity. 783-789 - Lavanya Sita Tekumalla, Sharmistha Jat:
IISCNLP at SemEval-2016 Task 2: Interpretable STS with ILP based Multiple Chunk Aligner. 790-795 - Rodolfo Delmonte:
VENSESEVAL at Semeval-2016 Task 2 iSTS - with a full-fledged rule-based approach. 796-802 - Miloslav Konopík, Ondrej Prazák, David Steinberger, Tomás Brychcín:
UWB at SemEval-2016 Task 2: Interpretable Semantic Textual Similarity with Distributional Semantics for Chunks. 803-808 - Rajendra Banjade, Nabin Maharjan, Nobal Bikram Niraula, Vasile Rus:
DTSim at SemEval-2016 Task 2: Interpreting Similarity of Texts Based on Automated Chunking, Chunk Alignment and Semantic Relation Prediction. 809-813 - Marc Franco-Salvador, Sudipta Kar, Thamar Solorio
UH-PRHLT at SemEval-2016 Task 3: Combining Lexical and Semantic-based Features for Community Question Answering. 814-821 - Ahmed Magooda
RDI_Team at SemEval-2016 Task 3: RDI Unsupervised Framework for Text Ranking. 822-827 - Mitra Mohtarami, Yonatan Belinkov, Wei-Ning Hsu, Yu Zhang, Tao Lei, Kfir Bar, Scott Cyphers, James R. Glass:
SLS at SemEval-2016 Task 3: Neural-based Approaches for Ranking in Community Question Answering. 828-835 - Tsvetomila Mihaylova
SUper Team at SemEval-2016 Task 3: Building a Feature-Rich System for Community Question Answering. 836-843 - Daniel Balchev, Yasen Kiprov, Ivan Koychev, Preslav Nakov:
PMI-cool at SemEval-2016 Task 3: Experiments with PMI and Goodness Polarity Lexicons for Community Question Answering. 844-850 - Timothy Baldwin, Huizhi Liang, Bahar Salehi, Doris Hoogeveen, Yitong Li, Long Duong:
UniMelb at SemEval-2016 Task 3: Identifying Similar Questions by combining a CNN with String Similarity Measures. 851-856 - Yunfang Wu, Minghua Zhang:
ICL00 at SemEval-2016 Task 3: Translation-Based Method for CQA System. 857-860 - Hujie Wang, Pascal Poupart:
Overfitting at SemEval-2016 Task 3: Detecting Semantically Similar Questions in Community Question Answering Forums with Word Embeddings. 861-865 - Rana Malhas, Marwan Torki
QU-IR at SemEval 2016 Task 3: Learning to Rank on Arabic Community Question Answering Forums with Word Embedding. 866-871 - GuoShun Wu, Man Lan:
ECNU at SemEval-2016 Task 3: Exploring Traditional Method and Deep Learning Method for Question Retrieval and Answer Ranking in Community Question Answering. 872-878 - Todor Mihaylov, Preslav Nakov:
SemanticZ at SemEval-2016 Task 3: Ranking Relevant Answers in Community Question Answering Using Semantic Similarity Based on Fine-tuned Word Embeddings. 879-886 - Francisco Guzmán, Preslav Nakov, Lluís Màrquez:
MTE-NN at SemEval-2016 Task 3: Can Machine Translation Evaluation Help Community Question Answering? 887-895 - Alberto Barrón-Cedeño, Giovanni Da San Martino, Shafiq R. Joty, Alessandro Moschitti, Fahad Al-Obaidli, Salvatore Romeo, Kateryna Tymoshenko, Antonio Uva:
ConvKN at SemEval-2016 Task 3: Answer and Question Selection for Question Answering on Arabic and English Fora. 896-903 - Chang e Jia:
ITNLP-AiKF at SemEval-2016 Task 3 a quesiton answering system using community QA repository. 904-909 - Silvio Cordeiro, Carlos Ramisch, Aline Villavicencio:
UFRGS&LIF at SemEval-2016 Task 10: Rule-Based MWE Identification and Predominant-Supersense Tagging. 910-917 - Xin Tang, Fei Li, Dong-Hong Ji:
WHUNlp at SemEval-2016 Task DiMSUM: A Pilot Study in Detecting Minimal Semantic Units and their Meanings using Supervised Models. 918-924 - Jari Björne, Tapio Salakoski:
UTU at SemEval-2016 Task 10: Binary Classification for Expression Detection (BCED). 925-930 - Mohammad Javad Hosseini, Noah A. Smith, Su-In Lee:
UW-CSE at SemEval-2016 Task 10: Detecting Multiword Expressions and Supersenses using Double-Chained Conditional Random Fields. 931-936 - Angelika Kirilin, Felix Krauss, Yannick Versley:
ICL-HD at SemEval-2016 Task 10: Improving the Detection of Minimal Semantic Units and their Meanings with an Ontology and Word Embeddings. 937-945 - Andreas Scherbakov, Ekaterina Vylomova, Fei Liu, Timothy Baldwin:
VectorWeavers at SemEval-2016 Task 10: From Incremental Meaning to Semantic Unit (phrase by phrase). 946-952 - Krzysztof Wróbel
PLUJAGH at SemEval-2016 Task 11: Simple System for Complex Word Identification. 953-957 - José Manuel Martínez Martínez, Liling Tan:
USAAR at SemEval-2016 Task 11: Complex Word Identification with Sense Entropy and Sentence Perplexity. 958-962 - Gillin Nat:
Sensible at SemEval-2016 Task 11: Neural Nonsense Mangled in Ensemble Mess. 963-968 - Gustavo Paetzold, Lucia Specia:
SV000gg at SemEval-2016 Task 11: Heavy Gauge Complex Word Identification with System Voting. 969-974 - Julian Brooke, Alexandra L. Uitdenbogerd, Timothy Baldwin:
Melbourne at SemEval 2016 Task 11: Classifying Type-level Word Complexity using Random Forests with Corpus and Word List Features. 975-981 - Elnaz Davoodi, Leila Kosseim:
CLaC at SemEval-2016 Task 11: Exploring linguistic and psycho-linguistic Features for Complex Word Identification. 982-985 - Niloy Mukherjee, Braja Gopal Patra
JU_NLP at SemEval-2016 Task 11: Identifying Complex Words in a Sentence. 986-990 - Shervin Malmasi, Marcos Zampieri:
MAZA at SemEval-2016 Task 11: Detecting Lexical Complexity Using a Decision Stump Meta-Classifier. 991-995 - Shervin Malmasi, Mark Dras
LTG at SemEval-2016 Task 11: Complex Word Identification with Classifier Ensembles. 996-1000 - Marcos Zampieri, Liling Tan, Josef van Genabith:
MacSaar at SemEval-2016 Task 11: Zipfian and Character Features for ComplexWord Identification. 1001-1005 - Prafulla Choubey, Shubham Pateria:
Garuda & Bhasha at SemEval-2016 Task 11: Complex Word Identification Using Aggregated Learning Models. 1006-1010 - Francesco Ronzano, Ahmed AbuRa'ed, Luis Espinosa Anke, Horacio Saggion:
TALN at SemEval-2016 Task 11: Modelling Complex Words by Contextual, Lexical and Semantic Features. 1011-1016 - Ashish Palakurthi, Radhika Mamidi
IIIT at SemEval-2016 Task 11: Complex Word Identification using Nearest Centroid Classification. 1017-1021 - Sanjay S. P., Anand Kumar M, K. P. Soman:
AmritaCEN at SemEval-2016 Task 11: Complex Word Identification using Word Embedding. 1022-1027 - Joachim Bingel
CoastalCPH at SemEval-2016 Task 11: The importance of designing your Neural Networks right. 1028-1033 - Maury Quijada, Julie Medero
HMC at SemEval-2016 Task 11: Identifying Complex Words Using Depth-limited Decision Trees. 1034-1037 - Michal Konkol:
UWB at SemEval-2016 Task 11: Exploring Features for Complex Word Identification. 1038-1041 - Onur Kuru:
AI-KU at SemEval-2016 Task 11: Word Embeddings and Substring Features for Complex Word Identification. 1042-1046 - David Kauchak:
Pomona at SemEval-2016 Task 11: Predicting Word Complexity Based on Corpus Frequency. 1047-1051 - Steven Bethard, Guergana Savova, Wei-Te Chen, Leon Derczynski, James Pustejovsky, Marc Verhagen:
SemEval-2016 Task 12: Clinical TempEval. - Jonathan May:
SemEval-2016 Task 8: Meaning Representation Parsing. 1063-1073 - Wanxiang Che, Yanqiu Shao, Ting Liu, Yu Ding:
SemEval-2016 Task 9: Chinese Semantic Dependency Parsing. 1074-1080 - Georgeta Bordea, Els Lefever, Paul Buitelaar:
SemEval-2016 Task 13: Taxonomy Extraction Evaluation (TExEval-2). 1081-1091 - David Jurgens, Mohammad Taher Pilehvar:
SemEval-2016 Task 14: Semantic Taxonomy Enrichment. 1092-1102 - Hua He, John Wieting, Kevin Gimpel, Jinfeng Rao, Jimmy Lin:
UMD-TTIC-UW at SemEval-2016 Task 1: Attention-Based Multi-Perspective Convolutional Neural Networks for Textual Similarity Measurement. 1103-1108 - Mishal Kazmi, Peter Schüller
Inspire at SemEval-2016 Task 2: Interpretable Semantic Textual Similarity Alignment based on Answer Set Programming. 1109-1115 - Simone Filice, Danilo Croce, Alessandro Moschitti, Roberto Basili:
KeLP at SemEval-2016 Task 3: Learning Semantic Relations between Questions and Answers. 1116-1123 - Jan Deriu
SwissCheese at SemEval-2016 Task 4: Sentiment Classification Using an Ensemble of Convolutional Neural Networks with Distant Supervision. 1124-1128 - Ayush Kumar, Sarah Kohail
IIT-TUDA at SemEval-2016 Task 5: Beyond Sentiment Lexicon: Combining Domain Dependency and Distributional Semantics Features for Aspect Based Sentiment Analysis. 1129-1135 - Julien Tourille, Olivier Ferret, Aurélie Névéol, Xavier Tannier:
LIMSI-COT at SemEval-2016 Task 12: Temporal relation identification using a pipeline of classifiers. 1136-1142 - Guntis Barzdins, Didzis Gosko:
RIGA at SemEval-2016 Task 8: Impact of Smatch Extensions and Character-Level Neural Translation on AMR Parsing Accuracy. 1143-1147 - Alastair Butler:
DynamicPower at SemEval-2016 Task 8: Processing syntactic parse trees with a Dynamic Semantics core. 1148-1153 - Yevgeniy Puzikov, Daisuke Kawahara, Sadao Kurohashi:
M2L at SemEval-2016 Task 8: AMR Parsing with Neural Networks. 1154-1159 - Lauritz Brandt, David Grimm, Mengfei Zhou, Yannick Versley:
ICL-HD at SemEval-2016 Task 8: Meaning Representation Parsing - Augmenting AMR Parsing with a Preposition Semantic Role Labeling Neural Network. 1160-1166 - James Goodman, Andreas Vlachos
UCL+Sheffield at SemEval-2016 Task 8: Imitation learning for AMR parsing with an alpha-bound. 1167-1172 - Chuan Wang, Sameer Pradhan, Xiaoman Pan, Heng Ji, Nianwen Xue:
CAMR at SemEval-2016 Task 8: An Extended Transition-based AMR Parser. 1173-1178 - Johannes Bjerva
The Meaning Factory at SemEval-2016 Task 8: Producing AMRs with Boxer. 1179-1184 - Xiaochang Peng, Daniel Gildea:
UofR at SemEval-2016 Task 8: Learning Synchronous Hyperedge Replacement Grammar for AMR Parsing. 1185-1189 - Sudha Rao, Yogarshi Vyas, Hal Daumé III, Philip Resnik:
CLIP$@$UMD at SemEval-2016 Task 8: Parser for Abstract Meaning Representation using Learning to Search. 1190-1196 - William Foland, James H. Martin
CU-NLP at SemEval-2016 Task 8: AMR Parsing using LSTM-based Recurrent Neural Networks. 1197-1201 - Jeffrey Flanigan
CMU at SemEval-2016 Task 8: Graph-based AMR Parsing with Infinite Ramp Loss. 1202-1206 - Artsiom Artsymenia, Palina Dounar, Maria Yermakovich:
IHS-RD-Belarus at SemEval-2016 Task 9: Transition-based Chinese Semantic Dependency Parsing with Online Reordering and Bootstrapping. 1207-1211 - Lifeng Jin, Manjuan Duan, William Schuler:
OCLSP at SemEval-2016 Task 9: Multilayered LSTM as a Neural Semantic Dependency Parser. 1212-1217 - Manjuan Duan, Lifeng Jin, William Schuler:
OSU_CHGCG at SemEval-2016 Task 9 : Chinese Semantic Dependency Parsing with Generalized Categorial Grammar. 1218-1224 - Cyril Grouin, Véronique Moriceau:
LIMSI at SemEval-2016 Task 12: machine-learning and temporal information to identify clinical events and time expressions. 1225-1230 - Sarath P. R., Manikandan R, Yoshiki Niwa:
Hitachi at SemEval-2016 Task 12: A Hybrid Approach for Temporal Information Extraction from Clinical Notes. 1231-1236 - Veera Raghavendra Chikka:
CDE-IIITH at SemEval-2016 Task 12: Extraction of Temporal Information from Clinical documents using Machine Learning techniques. 1237-1240 - Tommaso Caselli, Roser Morante:
VUACLTL at SemEval 2016 Task 12: A CRF Pipeline to Clinical TempEval. 1241-1247 - Arman Cohan
GUIR at SemEval-2016 task 12: Temporal Information Processing for Clinical Narratives. 1248-1255 - Abdulrahman Al Abdulsalam, Sumithra Velupillai, Stéphane M. Meystre:
UtahBMI at SemEval-2016 Task 12: Extracting Temporal Information from Clinical Text. 1256-1262 - Márcia Barros
ULISBOA at SemEval-2016 Task 12: Extraction of temporal expressions, clinical events and relations using IBEnt. 1263-1267 - Peng Li, Heng Huang:
UTA DLNLP at SemEval-2016 Task 12: Deep Learning Based Natural Language Processing System for Clinical Information Identification from Clinical Notes and Pathology Reports. 1268-1273 - Jason Alan Fries
Brundlefly at SemEval-2016 Task 12: Recurrent Neural Networks vs. Joint Inference for Clinical Temporal Information Extraction. 1274-1279 - Artuur Leeuwenberg
KULeuven-LIIR at SemEval 2016 Task 12: Detecting Narrative Containment in Clinical Records. 1280-1285 - Charlotte Hansart, Damien De Meyere, Patrick Watrin, André Bittar, Cédrick Fairon:
CENTAL at SemEval-2016 Task 12: a linguistically fed CRF model for medical and temporal information extraction. 1286-1291 - Hee-Jin Lee, Hua Xu, Jingqi Wang, Yaoyun Zhang, Sungrim Moon, Jun Xu, Yonghui Wu:
UTHealth at SemEval-2016 Task 12: an End-to-End System for Temporal Information Extraction from Clinical Notes. 1292-1297 - Joel Pocostales:
NUIG-UNLP at SemEval-2016 Task 13: A Simple Word Embedding-based Approach for Taxonomy Extraction. 1298-1302 - Liling Tan, Francis Bond
USAAR at SemEval-2016 Task 13: Hyponym Endocentricity. 1303-1309 - Promita Maitra, Dipankar Das:
JUNLP at SemEval-2016 Task 13: A Language Independent Approach for Hypernym Identification. 1310-1314 - Guillaume Cleuziou, José G. Moreno:
QASSIT at SemEval-2016 Task 13: On the integration of Semantic Vectors in Pretopological Spaces for Lexical Taxonomy Acquisition. 1315-1319 - Alexander Panchenko, Stefano Faralli
TAXI at SemEval-2016 Task 13: a Taxonomy Induction Method based on Lexico-Syntactic Patterns, Substrings and Focused Crawling. 1320-1327 - Ted Pedersen:
Duluth at SemEval 2016 Task 14: Extending Gloss Overlaps to Enrich Semantic Taxonomies. 1328-1331 - Luis Espinosa Anke, Francesco Ronzano, Horacio Saggion:
TALN at SemEval-2016 Task 14: Semantic Taxonomy Enrichment Via Sense-Based Embeddings. 1332-1336 - Michael Sejr Schlichtkrull, Héctor Martínez Alonso:
MSejrKu at SemEval-2016 Task 14: Taxonomy Enrichment by Evidence Ranking. 1337-1341 - Hristo Tanev, Agata Rotondi:
Deftor at SemEval-2016 Task 14: Taxonomy enrichment using definition vectors. 1342-1345 - Jon Rusert, Ted Pedersen:
UMNDuluth at SemEval-2016 Task 14: WordNet's Missing Lemmas. 1346-1350 - Bridget T. McInnes:
VCU at Semeval-2016 Task 14: Evaluating definitional-based similarity measure for semantic taxonomy enrichment. 1351-1355
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