Lexical and Computational Semantics and Semantic Evaluation (formerly Workshop on Sense Evaluation) (2016)
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)
Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)
Steven Bethard
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Marine Carpuat
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Daniel Cer
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David Jurgens
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Preslav Nakov
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Torsten Zesch
SemEval-2016 Task 4: Sentiment Analysis in Twitter
Preslav Nakov
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Alan Ritter
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Sara Rosenthal
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Fabrizio Sebastiani
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Veselin Stoyanov
SemEval-2016 Task 5: Aspect Based Sentiment Analysis
Maria Pontiki
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Dimitris Galanis
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Haris Papageorgiou
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Ion Androutsopoulos
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Suresh Manandhar
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Mohammad AL-Smadi
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Mahmoud Al-Ayyoub
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Yanyan Zhao
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Bing Qin
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Orphée De Clercq
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Véronique Hoste
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Marianna Apidianaki
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Xavier Tannier
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Natalia Loukachevitch
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Evgeniy Kotelnikov
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Nuria Bel
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Salud María Jiménez-Zafra
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Gülşen Eryiğit
SemEval-2016 Task 6: Detecting Stance in Tweets
Saif Mohammad
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Svetlana Kiritchenko
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Parinaz Sobhani
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Xiaodan Zhu
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Colin Cherry
SemEval-2016 Task 7: Determining Sentiment Intensity of English and Arabic Phrases
Svetlana Kiritchenko
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Saif Mohammad
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Mohammad Salameh
CUFE at SemEval-2016 Task 4: A Gated Recurrent Model for Sentiment Classification
Mahmoud Nabil
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Amir Atyia
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Mohamed Aly
QCRI at SemEval-2016 Task 4: Probabilistic Methods for Binary and Ordinal Quantification
Giovanni Da San Martino
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Wei Gao
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Fabrizio Sebastiani
SteM at SemEval-2016 Task 4: Applying Active Learning to Improve Sentiment Classification
Stefan Räbiger
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Mishal Kazmi
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Yücel Saygın
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Peter Schüller
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Myra Spiliopoulou
I2RNTU at SemEval-2016 Task 4: Classifier Fusion for Polarity Classification in Twitter
Zhengchen Zhang
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Chen Zhang
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Fuxiang Wu
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Dong-Yan Huang
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Weisi Lin
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Minghui Dong
LyS at SemEval-2016 Task 4: Exploiting Neural Activation Values for Twitter Sentiment Classification and Quantification
David Vilares
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Yerai Doval
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Miguel A. Alonso
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Carlos Gómez-Rodríguez
TwiSE at SemEval-2016 Task 4: Twitter Sentiment Classification
Georgios Balikas
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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
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Apostolos Rousas
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John Pavlopoulos
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Prodromos Malakasiotis
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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
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Valerij Fredriksen
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Björn Gambäck
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Lars Bungum
UDLAP at SemEval-2016 Task 4: Sentiment Quantification Using a Graph Based Representation
Esteban Castillo
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Ofelia Cervantes
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Darnes Vilariño
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David Báez
GTI at SemEval-2016 Task 4: Training a Naive Bayes Classifier using Features of an Unsupervised System
Jonathan Juncal-Martínez
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Tamara Álvarez-López
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Milagros Fernández-Gavilanes
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Enrique Costa-Montenegro
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Francisco Javier González-Castaño
Aicyber at SemEval-2016 Task 4: i-vector based sentence representation
Steven Du
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Xi Zhang
PUT at SemEval-2016 Task 4: The ABC of Twitter Sentiment Analysis
Mateusz Lango
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Dariusz Brzezinski
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Jerzy Stefanowski
mib at SemEval-2016 Task 4a: Exploiting lexicon based features for Sentiment Analysis in Twitter
Vittoria Cozza
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Marinella Petrocchi
MDSENT at SemEval-2016 Task 4: A Supervised System for Message Polarity Classification
Hang Gao
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Tim Oates
CICBUAPnlp at SemEval-2016 Task 4-A: Discovering Twitter Polarity using Enhanced Embeddings
Helena Gomez
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Darnes Vilariño
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Grigori Sidorov
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David Pinto Avendaño
Finki at SemEval-2016 Task 4: Deep Learning Architecture for Twitter Sentiment Analysis
Dario Stojanovski
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Gjorgji Strezoski
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Gjorgji Madjarov
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Ivica Dimitrovski
Tweester at SemEval-2016 Task 4: Sentiment Analysis in Twitter Using Semantic-Affective Model Adaptation
Elisavet Palogiannidi
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Athanasia Kolovou
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Fenia Christopoulou
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Filippos Kokkinos
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Elias Iosif
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Nikolaos Malandrakis
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Haris Papageorgiou
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Shrikanth Narayanan
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Alexandros Potamianos
UofL at SemEval-2016 Task 4: Multi Domain word2vec for Twitter Sentiment Classification
Omar Abdelwahab
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Adel Elmaghraby
NRU-HSE at SemEval-2016 Task 4: Comparative Analysis of Two Iterative Methods Using Quantification Library
Nikolay Karpov
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Alexander Porshnev
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Kirill Rudakov
INSIGHT-1 at SemEval-2016 Task 4: Convolutional Neural Networks for Sentiment Classification and Quantification
Sebastian Ruder
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Parsa Ghaffari
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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
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HuiZhi Liang
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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
Víctor Martinez Morant
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LLuís-F. Hurtado
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Ferran Pla
SENSEI-LIF at SemEval-2016 Task 4: Polarity embedding fusion for robust sentiment analysis
Mickael Rouvier
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Benoit Favre
DiegoLab16 at SemEval-2016 Task 4: Sentiment Analysis in Twitter using Centroids, Clusters, and Sentiment Lexicons
Abeed Sarker
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Graciela Gonzalez
VCU-TSA at Semeval-2016 Task 4: Sentiment Analysis in Twitter
Gerard Briones
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Kasun Amarasinghe
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Bridget McInnes
UniPI at SemEval-2016 Task 4: Convolutional Neural Networks for Sentiment Classification
Giuseppe Attardi
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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
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Ramon F. Astudillo
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Wang Ling
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Mário J. Silva
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Isabel Trancoso
SentimentalITsts at SemEval-2016 Task 4: building a Twitter sentiment analyzer in your backyard
Cosmin Florean
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Oana Bejenaru
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Eduard Apostol
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Octavian Ciobanu
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Adrian Iftene
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Diana Trandabăţ
Minions at SemEval-2016 Task 4: or how to build a sentiment analyzer using off-the-shelf resources?
Călin-Cristian Ciubotariu
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Marius-Valentin Hrişca
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Mihail Gliga
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Diana Darabană
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Diana Trandabăţ
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Adrian Iftene
YZU-NLP Team at SemEval-2016 Task 4: Ordinal Sentiment Classification Using a Recurrent Convolutional Network
Yunchao He
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Liang-Chih Yu
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Chin-Sheng Yang
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K. Robert Lai
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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
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Zhihua Zhang
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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
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Andi Rexha
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Roman Kern
NileTMRG at SemEval-2016 Task 5: Deep Convolutional Neural Networks for Aspect Category and Sentiment Extraction
Talaat Khalil
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Samhaa R. El-Beltagy
XRCE at SemEval-2016 Task 5: Feedbacked Ensemble Modeling on Syntactico-Semantic Knowledge for Aspect Based Sentiment Analysis
Caroline Brun
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Julien Perez
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Claude Roux
NLANGP at SemEval-2016 Task 5: Improving Aspect Based Sentiment Analysis using Neural Network Features
Zhiqiang Toh
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Jian Su
bunji at SemEval-2016 Task 5: Neural and Syntactic Models of Entity-Attribute Relationship for Aspect-based Sentiment Analysis
Toshihiko Yanase
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Kohsuke Yanai
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Misa Sato
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Toshinori Miyoshi
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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 Macháček
GTI at SemEval-2016 Task 5: SVM and CRF for Aspect Detection and Unsupervised Aspect-Based Sentiment Analysis
Tamara Álvarez-López
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Jonathan Juncal-Martínez
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Milagros Fernández-Gavilanes
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Enrique Costa-Montenegro
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Francisco Javier González-Castaño
AUEB-ABSA at SemEval-2016 Task 5: Ensembles of Classifiers and Embeddings for Aspect Based Sentiment Analysis
Dionysios Xenos
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Panagiotis Theodorakakos
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John Pavlopoulos
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Prodromos Malakasiotis
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Ion Androutsopoulos
AKTSKI at SemEval-2016 Task 5: Aspect Based Sentiment Analysis for Consumer Reviews
Shubham Pateria
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Prafulla Choubey
MayAnd at SemEval-2016 Task 5: Syntactic and word2vec-based approach to aspect-based polarity detection in Russian
Vladimir Mayorov
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Ivan Andrianov
INSIGHT-1 at SemEval-2016 Task 5: Deep Learning for Multilingual Aspect-based Sentiment Analysis
Sebastian Ruder
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Parsa Ghaffari
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John G. Breslin
TGB at SemEval-2016 Task 5: Multi-Lingual Constraint System for Aspect Based Sentiment Analysis
Fatih Samet Çetin
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Ezgi Yıldırım
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Can Özbey
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Gülşen Eryiğit
UWB at SemEval-2016 Task 5: Aspect Based Sentiment Analysis
Tomáš Hercig
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Tomáš Brychcín
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Lukáš Svoboda
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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
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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
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Zhihua Zhang
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Man Lan
UFAL at SemEval-2016 Task 5: Recurrent Neural Networks for Sentence Classification
Aleš Tamchyna
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Kateřina Veselovská
UWaterloo at SemEval-2016 Task 5: Minimally Supervised Approaches to Aspect-Based Sentiment Analysis
Olga Vechtomova
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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
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Karin Becker
pkudblab at SemEval-2016 Task 6 : A Specific Convolutional Neural Network System for Effective Stance Detection
Wan Wei
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Xiao Zhang
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Xuqin Liu
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Wei Chen
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Tengjiao Wang
USFD at SemEval-2016 Task 6: Any-Target Stance Detection on Twitter with Autoencoders
Isabelle Augenstein
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Andreas Vlachos
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Kalina Bontcheva
IUCL at SemEval-2016 Task 6: An Ensemble Model for Stance Detection in Twitter
Can Liu
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Wen Li
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Bradford Demarest
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Yue Chen
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Sara Couture
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Daniel Dakota
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Nikita Haduong
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Noah Kaufman
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Andrew Lamont
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Manan Pancholi
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Kenneth Steimel
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Sandra Kübler
Tohoku at SemEval-2016 Task 6: Feature-based Model versus Convolutional Neural Network for Stance Detection
Yuki Igarashi
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Hiroya Komatsu
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Sosuke Kobayashi
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Naoaki Okazaki
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Kentaro Inui
UWB at SemEval-2016 Task 6: Stance Detection
Peter Krejzl
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Josef Steinberger
DeepStance at SemEval-2016 Task 6: Detecting Stance in Tweets Using Character and Word-Level CNNs
Prashanth Vijayaraghavan
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Ivan Sysoev
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Soroush Vosoughi
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Deb Roy
NLDS-UCSC at SemEval-2016 Task 6: A Semi-Supervised Approach to Detecting Stance in Tweets
Amita Misra
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Brian Ecker
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Theodore Handleman
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Nicolas Hahn
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Marilyn Walker
ltl.uni-due at SemEval-2016 Task 6: Stance Detection in Social Media Using Stacked Classifiers
Michael Wojatzki
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Torsten Zesch
CU-GWU Perspective at SemEval-2016 Task 6: Ideological Stance Detection in Informal Text
Heba Elfardy
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Mona Diab
JU_NLP at SemEval-2016 Task 6: Detecting Stance in Tweets using Support Vector Machines
Braja Gopal Patra
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Dipankar Das
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Sivaji Bandyopadhyay
IDI@NTNU at SemEval-2016 Task 6: Detecting Stance in Tweets Using Shallow Features and GloVe Vectors for Word Representation
Henrik Bøhler
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Petter Asla
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Erwin Marsi
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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
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Man Lan
MITRE at SemEval-2016 Task 6: Transfer Learning for Stance Detection
Guido Zarrella
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Amy Marsh
TakeLab at SemEval-2016 Task 6: Stance Classification in Tweets Using a Genetic Algorithm Based Ensemble
Martin Tutek
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Ivan Sekulić
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Paula Gombar
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Ivan Paljak
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Filip Čulinović
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Filip Boltužić
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Vanja Mladen Karan
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Domagoj Alagić
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Jan Šnajder
LSIS at SemEval-2016 Task 7: Using Web Search Engines for English and Arabic Unsupervised Sentiment Intensity Prediction
Amal Htait
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Sebastien Fournier
|
Patrice Bellot
iLab-Edinburgh at SemEval-2016 Task 7: A Hybrid Approach for Determining Sentiment Intensity of Arabic Twitter Phrases
Eshrag Refaee
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Verena Rieser
UWB at SemEval-2016 Task 7: Novel Method for Automatic Sentiment Intensity Determination
Ladislav Lenc
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Pavel Král
|
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
|
Man Lan
SemEval-2016 Task 1: Semantic Textual Similarity, Monolingual and Cross-Lingual Evaluation
Eneko Agirre
|
Carmen Banea
|
Daniel Cer
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Mona Diab
|
Aitor Gonzalez-Agirre
|
Rada Mihalcea
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German Rigau
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Janyce Wiebe
SemEval-2016 Task 2: Interpretable Semantic Textual Similarity
Eneko Agirre
|
Aitor Gonzalez-Agirre
|
Iñigo Lopez-Gazpio
|
Montse Maritxalar
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German Rigau
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Larraitz Uria
SemEval-2016 Task 3: Community Question Answering
Preslav Nakov
|
Lluís Màrquez
|
Alessandro Moschitti
|
Walid Magdy
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Hamdy Mubarak
|
Abed Alhakim Freihat
|
Jim Glass
|
Bilal Randeree
SemEval-2016 Task 10: Detecting Minimal Semantic Units and their Meanings (DiMSUM)
Nathan Schneider
|
Dirk Hovy
|
Anders Johannsen
|
Marine Carpuat
SemEval 2016 Task 11: Complex Word Identification
Gustavo Paetzold
|
Lucia Specia
FBK HLT-MT at SemEval-2016 Task 1: Cross-lingual Semantic Similarity Measurement Using Quality Estimation Features and Compositional Bilingual Word Embeddings
Duygu Ataman
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José G. C. de Souza
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Marco Turchi
|
Matteo Negri
VRep at SemEval-2016 Task 1 and Task 2: A System for Interpretable Semantic Similarity
Sam Henry
|
Allison Sands
UTA DLNLP at SemEval-2016 Task 1: Semantic Textual Similarity: A Unified Framework for Semantic Processing and Evaluation
Peng Li
|
Heng Huang
UWB at SemEval-2016 Task 1: Semantic Textual Similarity using Lexical, Syntactic, and Semantic Information
Tomáš Brychcín
|
Lukáš Svoboda
HHU at SemEval-2016 Task 1: Multiple Approaches to Measuring Semantic Textual Similarity
Matthias Liebeck
|
Philipp Pollack
|
Pashutan Modaresi
|
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
|
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
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Kashif Shah
|
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
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Matthew Shardlow
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Georgios Kontonatsios
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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
|
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
|
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 Orăsan
|
Ruslan Mitkov
|
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
|
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
|
Le Sun
DLS@CU at SemEval-2016 Task 1: Supervised Models of Sentence Similarity
Md Arafat Sultan
|
Steven Bethard
|
Tamara Sumner
DCU-SEManiacs at SemEval-2016 Task 1: Synthetic Paragram Embeddings for Semantic Textual Similarity
Chris Hokamp
|
Piyush Arora
GWU NLP at SemEval-2016 Shared Task 1: Matrix Factorization for Crosslingual STS
Hanan Aldarmaki
|
Mona Diab
CNRC at SemEval-2016 Task 1: Experiments in Crosslingual Semantic Textual Similarity
Chi-kiu Lo
|
Cyril Goutte
|
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
|
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
|
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
|
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
|
Maryna Chernyshevich
JUNITMZ at SemEval-2016 Task 1: Identifying Semantic Similarity Using Levenshtein Ratio
Sandip Sarkar
|
Dipankar Das
|
Partha Pakray
|
Alexander Gelbukh
Amrita_CEN at SemEval-2016 Task 1: Semantic Relation from Word Embeddings in Higher Dimension
Barathi Ganesh HB
|
Anand Kumar M
|
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
|
Paul Buitelaar
NORMAS at SemEval-2016 Task 1: SEMSIM: A Multi-Feature Approach to Semantic Text Similarity
Kolawole Adebayo
|
Luigi Di Caro
|
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
|
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
|
Paul Cook
ASOBEK at SemEval-2016 Task 1: Sentence Representation with Character N-gram Embeddings for Semantic Textual Similarity
Asli Eyecioglu
|
Bill Keller
SimiHawk at SemEval-2016 Task 1: A Deep Ensemble System for Semantic Textual Similarity
Peter Potash
|
William Boag
|
Alexey Romanov
|
Vasili Ramanishka
|
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 Biçici
DalGTM at SemEval-2016 Task 1: Importance-Aware Compositional Approach to Short Text Similarity
Jie Mei
|
Aminul Islam
|
Evangelos Milios
iUBC at SemEval-2016 Task 2: RNNs and LSTMs for interpretable STS
Iñigo Lopez-Gazpio
|
Eneko Agirre
|
Montse Maritxalar
Rev at SemEval-2016 Task 2: Aligning Chunks by Lexical, Part of Speech and Semantic Equivalence
Ping Tan
|
Karin Verspoor
|
Timothy Miller
FBK-HLT-NLP at SemEval-2016 Task 2: A Multitask, Deep Learning Approach for Interpretable Semantic Textual Similarity
Simone Magnolini
|
Anna Feltracco
|
Bernardo Magnini
IISCNLP at SemEval-2016 Task 2: Interpretable STS with ILP based Multiple Chunk Aligner
Lavanya Tekumalla
|
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 Konopík
|
Ondřej Pražák
|
David Steinberger
|
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
|
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
|
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
|
Ahmad Al Sallab
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
|
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
|
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
|
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
|
Long Duong
ICL00 at SemEval-2016 Task 3: Translation-Based Method for CQA System
Yunfang Wu
|
Minghua Zhang
Overfitting at SemEval-2016 Task 3: Detecting Semantically Similar Questions in Community Question Answering Forums with Word Embeddings
Hujie Wang
|
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
|
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
|
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
|
Preslav Nakov
MTE-NN at SemEval-2016 Task 3: Can Machine Translation Evaluation Help Community Question Answering?
Francisco Guzmán
|
Preslav Nakov
|
Lluís Màrquez
ConvKN at SemEval-2016 Task 3: Answer and Question Selection for Question Answering on Arabic and English Fora
Alberto Barrón-Cedeño
|
Daniele Bonadiman
|
Giovanni Da San Martino
|
Shafiq Joty
|
Alessandro Moschitti
|
Fahad Al Obaidli
|
Salvatore Romeo
|
Kateryna Tymoshenko
|
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
|
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
|
Donghong Ji
UTU at SemEval-2016 Task 10: Binary Classification for Expression Detection (BCED)
Jari Björne
|
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
|
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
|
Yannick Versley
VectorWeavers at SemEval-2016 Task 10: From Incremental Meaning to Semantic Unit (phrase by phrase)
Andreas Scherbakov
|
Ekaterina Vylomova
|
Fei Liu
|
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
|
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
|
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
|
Timothy Baldwin
CLaC at SemEval-2016 Task 11: Exploring linguistic and psycho-linguistic Features for Complex Word Identification
Elnaz Davoodi
|
Leila Kosseim
JU_NLP at SemEval-2016 Task 11: Identifying Complex Words in a Sentence
Niloy Mukherjee
|
Braja Gopal Patra
|
Dipankar Das
|
Sivaji Bandyopadhyay
MAZA at SemEval-2016 Task 11: Detecting Lexical Complexity Using a Decision Stump Meta-Classifier
Shervin Malmasi
|
Marcos Zampieri
LTG at SemEval-2016 Task 11: Complex Word Identification with Classifier Ensembles
Shervin Malmasi
|
Mark Dras
|
Marcos Zampieri
MacSaar at SemEval-2016 Task 11: Zipfian and Character Features for ComplexWord Identification
Marcos Zampieri
|
Liling Tan
|
Josef van Genabith
Garuda & Bhasha at SemEval-2016 Task 11: Complex Word Identification Using Aggregated Learning Models
Prafulla Choubey
|
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
|
Horacio Saggion
IIIT at SemEval-2016 Task 11: Complex Word Identification using Nearest Centroid Classification
Ashish Palakurthi
|
Radhika Mamidi
AmritaCEN at SemEval-2016 Task 11: Complex Word Identification using Word Embedding
Sanjay S.P
|
Anand Kumar M
|
Soman K P
CoastalCPH at SemEval-2016 Task 11: The importance of designing your Neural Networks right
Joachim Bingel
|
Natalie Schluter
|
Héctor Martínez Alonso
HMC at SemEval-2016 Task 11: Identifying Complex Words Using Depth-limited Decision Trees
Maury Quijada
|
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
SemEval-2016 Task 12: Clinical TempEval
Steven Bethard
|
Guergana Savova
|
Wei-Te Chen
|
Leon Derczynski
|
James Pustejovsky
|
Marc Verhagen
SemEval-2016 Task 8: Meaning Representation Parsing
Jonathan May
SemEval-2016 Task 9: Chinese Semantic Dependency Parsing
Wanxiang Che
|
Yanqiu Shao
|
Ting Liu
|
Yu Ding
SemEval-2016 Task 13: Taxonomy Extraction Evaluation (TExEval-2)
Georgeta Bordea
|
Els Lefever
|
Paul Buitelaar
SemEval-2016 Task 14: Semantic Taxonomy Enrichment
David Jurgens
|
Mohammad Taher Pilehvar
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
|
Jimmy Lin
Inspire at SemEval-2016 Task 2: Interpretable Semantic Textual Similarity Alignment based on Answer Set Programming
Mishal Kazmi
|
Peter Schüller
KeLP at SemEval-2016 Task 3: Learning Semantic Relations between Questions and Answers
Simone Filice
|
Danilo Croce
|
Alessandro Moschitti
|
Roberto Basili
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
|
Martin Jaggi
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
|
Chris Biemann
LIMSI-COT at SemEval-2016 Task 12: Temporal relation identification using a pipeline of classifiers
Julien Tourille
|
Olivier Ferret
|
Aurélie Névéol
|
Xavier Tannier
RIGA at SemEval-2016 Task 8: Impact of Smatch Extensions and Character-Level Neural Translation on AMR Parsing Accuracy
Guntis Barzdins
|
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
|
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
|
Yannick Versley
UCL+Sheffield at SemEval-2016 Task 8: Imitation learning for AMR parsing with an alpha-bound
James Goodman
|
Andreas Vlachos
|
Jason Naradowsky
CAMR at SemEval-2016 Task 8: An Extended Transition-based AMR Parser
Chuan Wang
|
Sameer Pradhan
|
Xiaoman Pan
|
Heng Ji
|
Nianwen Xue
The Meaning Factory at SemEval-2016 Task 8: Producing AMRs with Boxer
Johannes Bjerva
|
Johan Bos
|
Hessel Haagsma
UofR at SemEval-2016 Task 8: Learning Synchronous Hyperedge Replacement Grammar for AMR Parsing
Xiaochang Peng
|
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
|
Philip Resnik
CU-NLP at SemEval-2016 Task 8: AMR Parsing using LSTM-based Recurrent Neural Networks
William Foland
|
James H. Martin
CMU at SemEval-2016 Task 8: Graph-based AMR Parsing with Infinite Ramp Loss
Jeffrey Flanigan
|
Chris Dyer
|
Noah A. Smith
|
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
|
Maria Yermakovich
OCLSP at SemEval-2016 Task 9: Multilayered LSTM as a Neural Semantic Dependency Parser
Lifeng Jin
|
Manjuan Duan
|
William Schuler
OSU_CHGCG at SemEval-2016 Task 9 : Chinese Semantic Dependency Parsing with Generalized Categorial Grammar
Manjuan Duan
|
Lifeng Jin
|
William Schuler
LIMSI at SemEval-2016 Task 12: machine-learning and temporal information to identify clinical events and time expressions
Cyril Grouin
|
Véronique Moriceau
Hitachi at SemEval-2016 Task 12: A Hybrid Approach for Temporal Information Extraction from Clinical Notes
Sarath P R
|
Manikandan R
|
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
|
Roser Morante
GUIR at SemEval-2016 task 12: Temporal Information Processing for Clinical Narratives
Arman Cohan
|
Kevin Meurer
|
Nazli Goharian
UtahBMI at SemEval-2016 Task 12: Extracting Temporal Information from Clinical Text
Abdulrahman Khalifa
|
Sumithra Velupillai
|
Stephane Meystre
ULISBOA at SemEval-2016 Task 12: Extraction of temporal expressions, clinical events and relations using IBEnt
Marcia Barros
|
Andre Lamurias
|
Gonçalo Figueiro
|
Marta Antunes
|
Joana Teixeira
|
Alexandre Pinheiro
|
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
|
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
|
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
|
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
|
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
|
Josef van Genabith
JUNLP at SemEval-2016 Task 13: A Language Independent Approach for Hypernym Identification
Promita Maitra
|
Dipankar Das
QASSIT at SemEval-2016 Task 13: On the integration of Semantic Vectors in Pretopological Spaces for Lexical Taxonomy Acquisition
Guillaume Cleuziou
|
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
|
Cédrick Fairon
|
Simone Paolo Ponzetto
|
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
|
Horacio Saggion
MSejrKu at SemEval-2016 Task 14: Taxonomy Enrichment by Evidence Ranking
Michael Schlichtkrull
|
Héctor Martínez Alonso
Deftor at SemEval-2016 Task 14: Taxonomy enrichment using definition vectors
Hristo Tanev
|
Agata Rotondi
UMNDuluth at SemEval-2016 Task 14: WordNet’s Missing Lemmas
Jon Rusert
|
Ted Pedersen
VCU at Semeval-2016 Task 14: Evaluating definitional-based similarity measure for semantic taxonomy enrichment
Bridget McInnes