16 Jun 2016 |
| 09:00–09:15 | Welcome |
| Opening Remarks
SemEval organizers |
| 09:15–10:30 | Sentiment Analysis |
| 09:15–09:30 | SemEval-2016 Task 4: Sentiment Analysis in Twitter
Preslav Nakov, Alan Ritter, Sara Rosenthal, Fabrizio Sebastiani and Veselin Stoyanov |
| 09:30–09:45 | 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 |
| 09:45–10:00 | SemEval-2016 Task 6: Detecting Stance in Tweets
Saif Mohammad, Svetlana Kiritchenko, Parinaz Sobhani, Xiaodan Zhu and Colin Cherry |
| 10:00–10:15 | SemEval-2016 Task 7: Determining Sentiment Intensity of English and Arabic Phrases
Svetlana Kiritchenko, Saif Mohammad and Mohammad Salameh |
| 10:15–10:30 | Sentiment Analysis Discussion
Task Organizers |
| 10:30–11:00 | Coffee Break |
| 11:00–12:30 | Poster Session: Sentiment Analysis |
| | CUFE at SemEval-2016 Task 4: A Gated Recurrent Model for Sentiment Classification
Mahmoud Nabil, Amir Atyia and Mohamed Aly |
| | QCRI at SemEval-2016 Task 4: Probabilistic Methods for Binary and Ordinal Quantification
Giovanni Da San Martino, Wei Gao and Fabrizio Sebastiani |
| | SteM at SemEval-2016 Task 4: Applying Active Learning to Improve Sentiment Classification
Stefan Räbiger, Mishal Kazmi, Yücel Saygın, Peter Schüller and Myra Spiliopoulou |
| | 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 |
| | 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 |
| | TwiSE at SemEval-2016 Task 4: Twitter Sentiment Classification
Georgios Balikas and Massih-Reza Amini |
| | ISTI-CNR at SemEval-2016 Task 4: Quantification on an Ordinal Scale
Andrea Esuli |
| | 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 |
| | thecerealkiller at SemEval-2016 Task 4: Deep Learning based System for Classifying Sentiment of Tweets on Two Point Scale
Vikrant Yadav |
| | NTNUSentEval at SemEval-2016 Task 4: Combining General Classifiers for Fast Twitter Sentiment Analysis
Brage Ekroll Jahren, Valerij Fredriksen, Björn Gambäck and Lars Bungum |
| | UDLAP at SemEval-2016 Task 4: Sentiment Quantification Using a Graph Based Representation
Esteban Castillo, Ofelia Cervantes, Darnes Vilariño and David Báez |
| | 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 |
| | Aicyber at SemEval-2016 Task 4: i-vector based sentence representation
Steven Du and Xi Zhang |
| | 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 |
| | PUT at SemEval-2016 Task 4: The ABC of Twitter Sentiment Analysis
Mateusz Lango, Dariusz Brzezinski and Jerzy Stefanowski |
| | mib at SemEval-2016 Task 4a: Exploiting lexicon based features for Sentiment Analysis in Twitter
Vittoria Cozza and Marinella Petrocchi |
| | MDSENT at SemEval-2016 Task 4: A Supervised System for Message Polarity Classification
Hang Gao and Tim Oates |
| | CICBUAPnlp at SemEval-2016 Task 4-A: Discovering Twitter Polarity using Enhanced Embeddings
Helena Gomez, Darnes Vilariño, Grigori Sidorov and David Pinto Avendaño |
| | Finki at SemEval-2016 Task 4: Deep Learning Architecture for Twitter Sentiment Analysis
Dario Stojanovski, Gjorgji Strezoski, Gjorgji Madjarov and Ivica Dimitrovski |
| | 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 |
| | UofL at SemEval-2016 Task 4: Multi Domain word2vec for Twitter Sentiment Classification
Omar Abdelwahab and Adel Elmaghraby |
| | NRU-HSE at SemEval-2016 Task 4: Comparative Analysis of Two Iterative Methods Using Quantification Library
Nikolay Karpov, Alexander Porshnev and Kirill Rudakov |
| | INSIGHT-1 at SemEval-2016 Task 4: Convolutional Neural Networks for Sentiment Classification and Quantification
Sebastian Ruder, Parsa Ghaffari and John G. Breslin |
| | 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 |
| | SentiSys at SemEval-2016 Task 4: Feature-Based System for Sentiment Analysis in Twitter
Hussam Hamdan |
| | 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 |
| | SENSEI-LIF at SemEval-2016 Task 4: Polarity embedding fusion for robust sentiment analysis
Mickael Rouvier and Benoit Favre |
| | DiegoLab16 at SemEval-2016 Task 4: Sentiment Analysis in Twitter using Centroids, Clusters, and Sentiment Lexicons
Abeed Sarker and Graciela Gonzalez |
| | VCU-TSA at Semeval-2016 Task 4: Sentiment Analysis in Twitter
Gerard Briones, Kasun Amarasinghe and Bridget McInnes |
| | UniPI at SemEval-2016 Task 4: Convolutional Neural Networks for Sentiment Classification
Giuseppe Attardi and Daniele Sartiano |
| | IIP at SemEval-2016 Task 4: Prioritizing Classes in Ensemble Classification for Sentiment Analysis of Tweets
Jasper Friedrichs |
| | PotTS at SemEval-2016 Task 4: Sentiment Analysis of Twitter Using Character-level Convolutional Neural Networks.
Uladzimir Sidarenka |
| | 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 |
| | 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 |
| | 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 |
| | 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 |
| | 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 |
| | OPAL at SemEval-2016 Task 4: the Challenge of Porting a Sentiment Analysis System to the "Real" World
Alexandra Balahur |
| | 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 |
| | NileTMRG at SemEval-2016 Task 5: Deep Convolutional Neural Networks for Aspect Category and Sentiment Extraction
Talaat Khalil and Samhaa R. El-Beltagy |
| | 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 |
| | NLANGP at SemEval-2016 Task 5: Improving Aspect Based Sentiment Analysis using Neural Network Features
Zhiqiang Toh and Jian Su |
| | 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 |
| | IHS-RD-Belarus at SemEval-2016 Task 5: Detecting Sentiment Polarity Using the Heatmap of Sentence
Maryna Chernyshevich |
| | BUTknot at SemEval-2016 Task 5: Supervised Machine Learning with Term Substitution Approach in Aspect Category Detection
Jakub Machacek |
| | 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 |
| | 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 |
| | 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 |
| | AKTSKI at SemEval-2016 Task 5: Aspect Based Sentiment Analysis for Consumer Reviews
Shubham Pateria and Prafulla Choubey |
| | MayAnd at SemEval-2016 Task 5: Syntactic and word2vec-based approach to aspect-based polarity detection in Russian
Vladimir Mayorov and Ivan Andrianov |
| | INSIGHT-1 at SemEval-2016 Task 5: Deep Learning for Multilingual Aspect-based Sentiment Analysis
Sebastian Ruder, Parsa Ghaffari and John G. Breslin |
| | 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 |
| | UWB at SemEval-2016 Task 5: Aspect Based Sentiment Analysis
Tomáš Hercig, Tomáš Brychcín, Lukáš Svoboda and Michal Konkol |
| | SentiSys at SemEval-2016 Task 5: Opinion Target Extraction and Sentiment Polarity Detection
Hussam Hamdan |
| | COMMIT at SemEval-2016 Task 5: Sentiment Analysis with Rhetorical Structure Theory
Kim Schouten and Flavius Frasincar |
| | 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 |
| | UFAL at SemEval-2016 Task 5: Recurrent Neural Networks for Sentence Classification
Aleš Tamchyna and Kateřina Veselovská |
| | UWaterloo at SemEval-2016 Task 5: Minimally Supervised Approaches to Aspect-Based Sentiment Analysis
Olga Vechtomova and Anni He |
| | 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 |
| | 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 |
| | USFD at SemEval-2016 Task 6: Any-Target Stance Detection on Twitter with Autoencoders
Isabelle Augenstein, Andreas Vlachos and Kalina Bontcheva |
| | 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 |
| | 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 |
| | UWB at SemEval-2016 Task 6: Stance Detection
Peter Krejzl and Josef Steinberger |
| | 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 |
| | 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 |
| | ltl.uni-due at SemEval-2016 Task 6: Stance Detection in Social Media Using Stacked Classifiers
Michael Wojatzki and Torsten Zesch |
| | CU-GWU Perspective at SemEval-2016 Task 6: Ideological Stance Detection in Informal Text
Heba Elfardy and Mona Diab |
| | JU_NLP at SemEval-2016 Task 6: Detecting Stance in Tweets using Support Vector Machines
Braja Gopal Patra, Dipankar Das and Sivaji Bandyopadhyay |
| | 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 |
| | 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 |
| | MITRE at SemEval-2016 Task 6: Transfer Learning for Stance Detection
Guido Zarrella and Amy Marsh |
| | 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 |
| | 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 |
| | iLab-Edinburgh at SemEval-2016 Task 7: A Hybrid Approach for Determining Sentiment Intensity of Arabic Twitter Phrases
Eshrag Refaee and Verena Rieser |
| | UWB at SemEval-2016 Task 7: Novel Method for Automatic Sentiment Intensity Determination
Ladislav Lenc, Pavel Král and Václav Rajtmajer |
| | NileTMRG at SemEval-2016 Task 7: Deriving Prior Polarities for Arabic Sentiment Terms
Samhaa R. El-Beltagy |
| | ECNU at SemEval-2016 Task 7: An Enhanced Supervised Learning Method for Lexicon Sentiment Intensity Ranking
Feixiang Wang, Zhihua Zhang and Man Lan |
| 12:30–02:00 | Lunch |
| 02:00–03:30 | Textual Similarity, Question Answering and Semantic Analysis |
| 02:00–02:15 | 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 |
| 02:15–02:30 | SemEval-2016 Task 2: Interpretable Semantic Textual Similarity
Eneko Agirre, Aitor Gonzalez-Agirre, Inigo Lopez-Gazpio, Montse Maritxalar, German Rigau and Larraitz Uria |
| 02:30–02:45 | 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 |
| 02:45–03:00 | SemEval-2016 Task 10: Detecting Minimal Semantic Units and their Meanings (DiMSUM)
Nathan Schneider, Dirk Hovy, Anders Johannsen and Marine Carpuat |
| 03:00–03:15 | SemEval 2016 Task 11: Complex Word Identification
Gustavo Paetzold and Lucia Specia |
| 03:15–03:30 | Textual Similarity and Question Answering Discussion
Task Organizers |
| 03:30–04:00 | Coffee Break |
| 04:00–05:30 | Poster Session: Textual Similarity, and Question Answering |
| | 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 |
| | VRep at SemEval-2016 Task 1 and Task 2: A System for Interpretable Semantic Similarity
Sam Henry and Allison Sands |
| | UTA DLNLP at SemEval-2016 Task 1: Semantic Textual Similarity: A Unified Framework for Semantic Processing and Evaluation
Peng Li and Heng Huang |
| | UWB at SemEval-2016 Task 1: Semantic Textual Similarity using Lexical, Syntactic, and Semantic Information
Tomáš Brychcín and Lukáš Svoboda |
| | HHU at SemEval-2016 Task 1: Multiple Approaches to Measuring Semantic Textual Similarity
Matthias Liebeck, Philipp Pollack, Pashutan Modaresi and Stefan Conrad |
| | 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 |
| | 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 |
| | 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 |
| | 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 |
| | 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 |
| | 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 |
| | 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 |
| | 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 |
| | 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 |
| | DLS@CU at SemEval-2016 Task 1: Supervised Models of Sentence Similarity
Md Arafat Sultan, Steven Bethard and Tamara Sumner |
| | DCU-SEManiacs at SemEval-2016 Task 1: Synthetic Paragram Embeddings for Semantic Textual Similarity
Chris Hokamp and Piyush Arora |
| | iUBC at SemEval-2016 Task 2: RNNs and LSTMs for interpretable STS
Inigo Lopez-Gazpio, Eneko Agirre and Montse Maritxalar |
| | Rev at SemEval-2016 Task 2: Aligning Chunks by Lexical, Part of Speech and Semantic Equivalence
ping tan, Karin Verspoor and Timothy Miller |
| | Inspire at SemEval-2016 Task 2: Interpretable Semantic Textual Similarity Alignment based on Answer Set Programming
Mishal Kazmi and Peter Schüller |
| | 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 |
| | IISCNLP at SemEval-2016 Task 2: Interpretable STS with ILP based Multiple Chunk Aligner
Lavanya Tekumalla and Sharmistha Jat |
| | VENSESEVAL at Semeval-2016 Task 2 iSTS - with a full-fledged rule-based approach
Rodolfo Delmonte |
| | UWB at SemEval-2016 Task 2: Interpretable Semantic Textual Similarity with Distributional Semantics for Chunks
Miloslav Konopik, Ondrej Prazak, David Steinberger and Tomáš Brychcín |
| | 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 |
| | 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 |
| | 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 |
| | KeLP at SemEval-2016 Task 3: Learning Semantic Relations between Questions and Answers
Simone Filice, Danilo Croce, Alessandro Moschitti and Roberto Basili |
| | 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 |
| | 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 |
| | 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 |
| | 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 |
| | ICL00 at SemEval-2016 Task 3: Translation-Based Method for CQA System
Yunfang Wu and Minghua Zhang |
| | Overfitting at SemEval-2016 Task 3: Detecting Semantically Similar Questions in Community Question Answering Forums with Word Embeddings
Hujie Wang and Pascal Poupart |
| | 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 |
| | 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 |
| | 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 |
| | MTE-NN at SemEval-2016 Task 3: Can Machine Translation Evaluation Help Community Question Answering?
Francisco Guzmán, Preslav Nakov and Lluís Màrquez |
| | 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 |
| | ITNLP-AiKF at SemEval-2016 Task 3 a quesiton answering system using community QA repository
Chang e Jia |
| | UFRGS&LIF at SemEval-2016 Task 10: Rule-Based MWE Identification and Predominant-Supersense Tagging
Silvio Cordeiro, Carlos Ramisch and Aline Villavicencio |
| | 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 |
| | UTU at SemEval-2016 Task 10: Binary Classification for Expression Detection (BCED)
Jari Björne and Tapio Salakoski |
| | 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 |
| | 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 |
| | VectorWeavers at SemEval-2016 Task 10: From Incremental Meaning to Semantic Unit (phrase by phrase)
Andreas Scherbakov, Ekaterina Vylomova, Fei Liu and Timothy Baldwin |
| | PLUJAGH at SemEval-2016 Task 11: Simple System for Complex Word Identification
Krzysztof Wróbel |
| | USAAR at SemEval-2016 Task 11: Complex Word Identification with Sense Entropy and Sentence Perplexity
José Manuel Martínez Martínez and Liling Tan |
| | Sensible at SemEval-2016 Task 11: Neural Nonsense Mangled in Ensemble Mess
Gillin Nat |
| | SV000gg at SemEval-2016 Task 11: Heavy Gauge Complex Word Identification with System Voting
Gustavo Paetzold and Lucia Specia |
| | 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 |
| | CLaC at SemEval-2016 Task 11: Exploring linguistic and psycho-linguistic Features for Complex Word Identification
Elnaz Davoodi and Leila Kosseim |
| | JU_NLP at SemEval-2016 Task 11: Identifying Complex Words in a Sentence
Niloy Mukherjee, Braja Gopal Patra, Dipankar Das and Sivaji Bandyopadhyay |
| | MAZA at SemEval-2016 Task 11: Detecting Lexical Complexity Using a Decision Stump Meta-Classifier
Shervin Malmasi and Marcos Zampieri |
| | LTG at SemEval-2016 Task 11: Complex Word Identification with Classifier Ensembles
Shervin Malmasi, Mark Dras and Marcos Zampieri |
| | MacSaar at SemEval-2016 Task 11: Zipfian and Character Features for ComplexWord Identification
Marcos Zampieri, Liling Tan and Josef van Genabith |
| | Garuda & Bhasha at SemEval-2016 Task 11: Complex Word Identification Using Aggregated Learning Models
Prafulla Choubey and Shubham Pateria |
| | 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 |
| | IIIT at SemEval-2016 Task 11: Complex Word Identification using Nearest Centroid Classification
Ashish Palakurthi and Radhika Mamidi |
| | AmritaCEN at SemEval-2016 Task 11: Complex Word Identification using Word Embedding
sanjay sp, Anand Kumar and Soman K P |
| | CoastalCPH at SemEval-2016 Task 11: The importance of designing your Neural Networks right
Joachim Bingel, Natalie Schluter and Héctor Martínez Alonso |
| | HMC at SemEval-2016 Task 11: Identifying Complex Words Using Depth-limited Decision Trees
Maury Quijada and Julie Medero |
| | UWB at SemEval-2016 Task 11: Exploring Features for Complex Word Identification
Michal Konkol |
| | AI-KU at SemEval-2016 Task 11: Word Embeddings and Substring Features for Complex Word Identification
Onur Kuru |
| | Pomona at SemEval-2016 Task 11: Predicting Word Complexity Based on Corpus Frequency
David Kauchak |
17 Jul 2016 |
| 09:00–10:30 | Perspectives |
| 09:00–09:30 | SemEval-2017 Preview
SemEval organizers |
| 09:30–10:30 | Invited Talk |
| 10:30–11:00 | Coffee Break |
| 11:00–12:30 | Semantic Analysis, Semantic Parsing and Semantic Taxonomy |
| 11:00–11:15 | SemEval-2016 Task 12: Clinical TempEval
Steven Bethard, Guergana Savova, Wei-Te Chen, Leon Derczynski, James Pustejovsky and Marc Verhagen |
| 11:15–11:30 | SemEval-2016 Task 8: Meaning Representation Parsing
Jonathan May |
| 11:30–11:45 | SemEval-2016 Task 9: Chinese Semantic Dependency Parsing
Wanxiang Che, Yanqiu Shao, Ting Liu and Yu Ding |
| 11:45–12:00 | SemEval-2016 Task 13: Taxonomy Extraction Evaluation (TExEval-2)
Georgeta Bordea, Els Lefever and Paul Buitelaar |
| 12:00–12:15 | SemEval-2016 Task 14: Semantic Taxonomy Enrichment
David Jurgens and Mohammad Taher Pilehvar |
| 12:30–02:00 | Lunch |
| 02:00–03:30 | Best Of SemEval |
| 03:15–03:30 | LIMSI-COT at SemEval-2016 Task 12: Temporal relation identification using a pipeline of classifiers
Julien Tourille, Olivier Ferret, Aurélie Névéol and Xavier Tannier |
| 03:30–04:00 | Coffee Break |
| 04:00–05:30 | Poster Session: Semantic Analysis, Parsing, and Taxonomy |
| | RIGA at SemEval-2016 Task 8: Impact of Smatch Extensions and Character-Level Neural Translation on AMR Parsing Accuracy
Guntis Barzdins and Didzis Gosko |
| | DynamicPower at SemEval-2016 Task 8: Processing syntactic parse trees with a Dynamic Semantics core
Alastair Butler |
| | M2L at SemEval-2016 Task 8: AMR Parsing with Neural Networks
Yevgeniy Puzikov, Daisuke Kawahara and Sadao Kurohashi |
| | 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 |
| | UCL+Sheffield at SemEval-2016 Task 8: Imitation learning for AMR parsing with an alpha-bound
James Goodman, Andreas Vlachos and Jason Naradowsky |
| | CAMR at SemEval-2016 Task 8: An Extended Transition-based AMR Parser
Chuan Wang, Sameer Pradhan, Xiaoman Pan, Heng Ji and Nianwen Xue |
| | The Meaning Factory at SemEval-2016 Task 8: Producing AMRs with Boxer
Johannes Bjerva, Johan Bos and Hessel Haagsma |
| | UofR at SemEval-2016 Task 8: Learning Synchronous Hyperedge Replacement Grammar for AMR Parsing
Xiaochang Peng and Daniel Gildea |
| | 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 |
| | CU-NLP at SemEval-2016 Task 8: AMR Parsing using LSTM-based Recurrent Neural Networks
William Foland and James H. Martin |
| | CMU at SemEval-2016 Task 8: Graph-based AMR Parsing with Infinite Ramp Loss
Jeffrey Flanigan, Chris Dyer, Noah A. Smith and Jaime Carbonell |
| | 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 |
| | OCLSP at SemEval-2016 Task 9: Multilayered LSTM as a Neural Semantic Dependency Parser
Lifeng Jin, Manjuan Duan and William Schuler |
| | OSU_CHGCG at SemEval-2016 Task 9 : Chinese Semantic Dependency Parsing with Generalized Categorial Grammar
Manjuan Duan, Lifeng Jin and William Schuler |
| | LIMSI at SemEval-2016 Task 12: machine-learning and temporal information to identify clinical events and time expressions
Cyril Grouin and Véronique MORICEAU |
| | Hitachi at SemEval-2016 Task 12: A Hybrid Approach for Temporal Information Extraction from Clinical Notes
Sarath P R, Manikandan R and Yoshiki Niwa |
| | CDE-IIITH at SemEval-2016 Task 12: Extraction of Temporal Information from Clinical documents using Machine Learning techniques
Veera Raghavendra Chikka |
| | VUACLTL at SemEval 2016 Task 12: A CRF Pipeline to Clinical TempEval
Tommaso Caselli and Roser Morante |
| | GUIR at SemEval-2016 task 12: Temporal Information Processing for Clinical Narratives
Arman Cohan, Kevin Meurer and Nazli Goharian |
| | UtahBMI at SemEval-2016 Task 12: Extracting Temporal Information from Clinical Text
Abdulrahman AAl Abdulsalam, Sumithra Velupillai and Stephane Meystre |
| | 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 |
| | 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 |
| | Brundlefly at SemEval-2016 Task 12: Recurrent Neural Networks vs. Joint Inference for Clinical Temporal Information Extraction
Jason Fries |
| | KULeuven-LIIR at SemEval 2016 Task 12: Detecting Narrative Containment in Clinical Records
Artuur Leeuwenberg and Marie-Francine Moens |
| | 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 |
| | 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 |
| | NUIG-UNLP at SemEval-2016 Task 13: A Simple Word Embedding-based Approach for Taxonomy Extraction
Joel Pocostales |
| | USAAR at SemEval-2016 Task 13: Hyponym Endocentricity
Liling Tan, Francis Bond and Josef van Genabith |
| | JUNLP at SemEval-2016 Task 13: A Language Independent Approach for Hypernym Identification
Promita Maitra and Dipankar Das |
| | 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 |
| | 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 |
| | Duluth at SemEval 2016 Task 14: Extending Gloss Overlaps to Enrich Semantic Taxonomies
Ted Pedersen |
| | TALN at SemEval-2016 Task 14: Semantic Taxonomy Enrichment Via Sense-Based Embeddings
Luis Espinosa Anke, Francesco Ronzano and Horacio Saggion |
| | MSejrKu at SemEval-2016 Task 14: Taxonomy Enrichment by Evidence Ranking
Michael Schlichtkrull and Héctor Martínez Alonso |
| | Deftor at SemEval-2016 Task 14: Taxonomy enrichment using definition vectors
Hristo Tanev and Agata Rotondi |
| | UMNDuluth at SemEval-2016 Task 14: WordNet’s Missing Lemmas
Jon Rusert and Ted Pedersen |
| | VCU at Semeval-2016 Task 14: Evaluating definitional-based similarity measure for semantic taxonomy enrichment
Bridget McInnes |
| | GWU NLP at SemEval-2016 Shared Task 1: Matrix Factorization for Crosslingual STS
Hanan Aldarmaki and Mona Diab |
| | CNRC at SemEval-2016 Task 1: Experiments in Crosslingual Semantic Textual Similarity
Chi-kiu Lo, Cyril Goutte and Michel Simard |
| | 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 |
| | 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 |
| | 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 |
| | RICOH at SemEval-2016 Task 1: IR-based Semantic Textual Similarity Estimation
Hideo Itoh |
| | IHS-RD-Belarus at SemEval-2016 Task 1: Multistage Approach for Measuring Semantic Similarity
Maryna Beliuha and Maryna Chernyshevich |
| | JUNITMZ at SemEval-2016 Task 1: Identifying Semantic Similarity Using Levenshtein Ratio
Sandip Sarkar, Dipankar Das, Partha Pakray and Alexander Gelbukh |
| | Amrita_CEN at SemEval-2016 Task 1: Semantic Relation from Word Embeddings in Higher Dimension
Barathi Ganesh HB, Anand Kumar M and Soman KP |
| | 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 |
| | NORMAS at SemEval-2016 Task 1: SEMSIM: A Multi-Feature Approach to Semantic Text Similarity
kolawole adebayo, Luigi Di Caro and Guido Boella |
| | 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 |
| | 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 |
| | ASOBEK at SemEval-2016 Task 1: Sentence Representation with Character N-gram Embeddings for Semantic Textual Similarity
Asli Eyecioglu and Bill Keller |
| | 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 |
| | SERGIOJIMENEZ at SemEval-2016 Task 1: Effectively Combining Paraphrase Database, String Matching, WordNet, and Word Embedding for Semantic Textual Similarity
Sergio Jimenez |
| | RTM at SemEval-2016 Task 1: Predicting Semantic Similarity with Referential Translation Machines and Related Statistics
Ergun Bicici |
| | DalGTM at SemEval-2016 Task 1: Importance-Aware Compositional Approach to Short Text Similarity
Jie Mei, Aminul Islam and Evangelos Milios |