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
|
Marine Carpuat
|
Daniel Cer
|
David Jurgens
|
Preslav Nakov
|
Torsten Zesch
SemEval-2016 Task 4: Sentiment Analysis in Twitter
Preslav Nakov
|
Alan Ritter
|
Sara Rosenthal
|
Fabrizio Sebastiani
|
Veselin Stoyanov
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
|
Orphée De Clercq
|
Véronique Hoste
|
Marianna Apidianaki
|
Xavier Tannier
|
Natalia Loukachevitch
|
Evgeniy Kotelnikov
|
Nuria Bel
|
Salud María Jiménez-Zafra
|
Gülşen Eryiğit
SemEval-2016 Task 6: Detecting Stance in Tweets
Saif Mohammad
|
Svetlana Kiritchenko
|
Parinaz Sobhani
|
Xiaodan Zhu
|
Colin Cherry
SemEval-2016 Task 7: Determining Sentiment Intensity of English and Arabic Phrases
Svetlana Kiritchenko
|
Saif Mohammad
|
Mohammad Salameh
CUFE at SemEval-2016 Task 4: A Gated Recurrent Model for Sentiment Classification
Mahmoud Nabil
|
Amir Atyia
|
Mohamed Aly
QCRI at SemEval-2016 Task 4: Probabilistic Methods for Binary and Ordinal Quantification
Giovanni Da San Martino
|
Wei Gao
|
Fabrizio Sebastiani
SteM at SemEval-2016 Task 4: Applying Active Learning to Improve Sentiment Classification
Stefan Räbiger
|
Mishal Kazmi
|
Yücel Saygın
|
Peter Schüller
|
Myra Spiliopoulou
I2RNTU at SemEval-2016 Task 4: Classifier Fusion for Polarity Classification in Twitter
Zhengchen Zhang
|
Chen Zhang
|
Fuxiang Wu
|
Dong-Yan Huang
|
Weisi Lin
|
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
|
Carlos Gómez-Rodríguez
TwiSE at SemEval-2016 Task 4: Twitter Sentiment Classification
Georgios Balikas
|
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
|
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
|
Lars Bungum
UDLAP at SemEval-2016 Task 4: Sentiment Quantification Using a Graph Based Representation
Esteban Castillo
|
Ofelia Cervantes
|
Darnes Vilariño
|
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
|
Francisco Javier González-Castaño
Aicyber at SemEval-2016 Task 4: i-vector based sentence representation
Steven Du
|
Xi Zhang
PUT at SemEval-2016 Task 4: The ABC of Twitter Sentiment Analysis
Mateusz Lango
|
Dariusz Brzezinski
|
Jerzy Stefanowski
mib at SemEval-2016 Task 4a: Exploiting lexicon based features for Sentiment Analysis in Twitter
Vittoria Cozza
|
Marinella Petrocchi
MDSENT at SemEval-2016 Task 4: A Supervised System for Message Polarity Classification
Hang Gao
|
Tim Oates
CICBUAPnlp at SemEval-2016 Task 4-A: Discovering Twitter Polarity using Enhanced Embeddings
Helena Gomez
|
Darnes Vilariño
|
Grigori Sidorov
|
David Pinto Avendaño
Finki at SemEval-2016 Task 4: Deep Learning Architecture for Twitter Sentiment Analysis
Dario Stojanovski
|
Gjorgji Strezoski
|
Gjorgji Madjarov
|
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
|
Alexandros Potamianos
UofL at SemEval-2016 Task 4: Multi Domain word2vec for Twitter Sentiment Classification
Omar Abdelwahab
|
Adel Elmaghraby
NRU-HSE at SemEval-2016 Task 4: Comparative Analysis of Two Iterative Methods Using Quantification Library
Nikolay Karpov
|
Alexander Porshnev
|
Kirill Rudakov
INSIGHT-1 at SemEval-2016 Task 4: Convolutional Neural Networks for Sentiment Classification and Quantification
Sebastian Ruder
|
Parsa Ghaffari
|
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
|
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
|
LLuís-F. Hurtado
|
Ferran Pla
SENSEI-LIF at SemEval-2016 Task 4: Polarity embedding fusion for robust sentiment analysis
Mickael Rouvier
|
Benoit Favre
DiegoLab16 at SemEval-2016 Task 4: Sentiment Analysis in Twitter using Centroids, Clusters, and Sentiment Lexicons
Abeed Sarker
|
Graciela Gonzalez
VCU-TSA at Semeval-2016 Task 4: Sentiment Analysis in Twitter
Gerard Briones
|
Kasun Amarasinghe
|
Bridget McInnes
UniPI at SemEval-2016 Task 4: Convolutional Neural Networks for Sentiment Classification
Giuseppe Attardi
|
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
|
Ramon F. Astudillo
|
Wang Ling
|
Mário J. Silva
|
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
|
Diana Trandabăţ
Minions at SemEval-2016 Task 4: or how to build a sentiment analyzer using off-the-shelf resources?
Călin-Cristian Ciubotariu
|
Marius-Valentin Hrişca
|
Mihail Gliga
|
Diana Darabană
|
Diana Trandabăţ
|
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
|
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
|
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
|
Roman Kern
NileTMRG at SemEval-2016 Task 5: Deep Convolutional Neural Networks for Aspect Category and Sentiment Extraction
Talaat Khalil
|
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
|
Claude Roux
NLANGP at SemEval-2016 Task 5: Improving Aspect Based Sentiment Analysis using Neural Network Features
Zhiqiang Toh
|
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
|
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
|
Jonathan Juncal-Martínez
|
Milagros Fernández-Gavilanes
|
Enrique Costa-Montenegro
|
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
|
Ion Androutsopoulos
AKTSKI at SemEval-2016 Task 5: Aspect Based Sentiment Analysis for Consumer Reviews
Shubham Pateria
|
Prafulla Choubey
MayAnd at SemEval-2016 Task 5: Syntactic and word2vec-based approach to aspect-based polarity detection in Russian
Vladimir Mayorov
|
Ivan Andrianov
INSIGHT-1 at SemEval-2016 Task 5: Deep Learning for Multilingual Aspect-based Sentiment Analysis
Sebastian Ruder
|
Parsa Ghaffari
|
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
|
Gülşen Eryiğit
UWB at SemEval-2016 Task 5: Aspect Based Sentiment Analysis
Tomáš Hercig
|
Tomáš Brychcín
|
Lukáš Svoboda
|
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
|
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
|
Man Lan
UFAL at SemEval-2016 Task 5: Recurrent Neural Networks for Sentence Classification
Aleš Tamchyna
|
Kateřina Veselovská
UWaterloo at SemEval-2016 Task 5: Minimally Supervised Approaches to Aspect-Based Sentiment Analysis
Olga Vechtomova
|
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
|
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
|
Tengjiao Wang
USFD at SemEval-2016 Task 6: Any-Target Stance Detection on Twitter with Autoencoders
Isabelle Augenstein
|
Andreas Vlachos
|
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
|
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
|
Kentaro Inui
UWB at SemEval-2016 Task 6: Stance Detection
Peter Krejzl
|
Josef Steinberger
DeepStance at SemEval-2016 Task 6: Detecting Stance in Tweets Using Character and Word-Level CNNs
Prashanth Vijayaraghavan
|
Ivan Sysoev
|
Soroush Vosoughi
|
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
|
Marilyn Walker
ltl.uni-due at SemEval-2016 Task 6: Stance Detection in Social Media Using Stacked Classifiers
Michael Wojatzki
|
Torsten Zesch
CU-GWU Perspective at SemEval-2016 Task 6: Ideological Stance Detection in Informal Text
Heba Elfardy
|
Mona Diab
JU_NLP at SemEval-2016 Task 6: Detecting Stance in Tweets using Support Vector Machines
Braja Gopal Patra
|
Dipankar Das
|
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
|
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
|
Man Lan
MITRE at SemEval-2016 Task 6: Transfer Learning for Stance Detection
Guido Zarrella
|
Amy Marsh
TakeLab at SemEval-2016 Task 6: Stance Classification in Tweets Using a Genetic Algorithm Based Ensemble
Martin Tutek
|
Ivan Sekulić
|
Paula Gombar
|
Ivan Paljak
|
Filip Čulinović
|
Filip Boltužić
|
Mladen Karan
|
Domagoj Alagić
|
Jan Šnajder
LSIS at SemEval-2016 Task 7: Using Web Search Engines for English and Arabic Unsupervised Sentiment Intensity Prediction
Amal Htait
|
Sebastien Fournier
|
Patrice Bellot
iLab-Edinburgh at SemEval-2016 Task 7: A Hybrid Approach for Determining Sentiment Intensity of Arabic Twitter Phrases
Eshrag Refaee
|
Verena Rieser
UWB at SemEval-2016 Task 7: Novel Method for Automatic Sentiment Intensity Determination
Ladislav Lenc
|
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
|
Mona Diab
|
Aitor Gonzalez-Agirre
|
Rada Mihalcea
|
German Rigau
|
Janyce Wiebe
SemEval-2016 Task 2: Interpretable Semantic Textual Similarity
Eneko Agirre
|
Aitor Gonzalez-Agirre
|
Iñigo Lopez-Gazpio
|
Montse Maritxalar
|
German Rigau
|
Larraitz Uria
SemEval-2016 Task 3: Community Question Answering
Preslav Nakov
|
Lluís Màrquez
|
Alessandro Moschitti
|
Walid Magdy
|
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
|
José G. C. de Souza
|
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
|
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
|
Matthew Shardlow
|
Georgios Kontonatsios
|
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
Proceedings of the Fifth Joint Conference on Lexical and Computational Semantics
Proceedings of the Fifth Joint Conference on Lexical and Computational Semantics
Claire Gardent
|
Raffaella Bernardi
|
Ivan Titov
Quantificational features in distributional word representations
Tal Linzen
|
Emmanuel Dupoux
|
Benjamin Spector
Automatic Identification of Aspectual Classes across Verbal Readings
Ingrid Falk
|
Fabienne Martin
Metaphor as a Medium for Emotion: An Empirical Study
Saif Mohammad
|
Ekaterina Shutova
|
Peter Turney
High-Fidelity Lexical Axiom Construction from Verb Glosses
Gene Kim
|
Lenhart Schubert
Implicit Semantic Roles in a Multilingual Setting
Jennifer Sikos
|
Yannick Versley
|
Anette Frank
Driving inversion transduction grammar induction with semantic evaluation
Meriem Beloucif
|
Dekai Wu
Natural Solution to FraCaS Entailment Problems
Lasha Abzianidze
How Factuality Determines Sentiment Inferences
Manfred Klenner
|
Simon Clematide
Sense Embedding Learning for Word Sense Induction
Linfeng Song
|
Zhiguo Wang
|
Haitao Mi
|
Daniel Gildea
Improving Zero-Shot-Learning for German Particle Verbs by using Training-Space Restrictions and Local Scaling
Maximilian Köper
|
Sabine Schulte im Walde
|
Max Kisselew
|
Sebastian Padó
When Hyperparameters Help: Beneficial Parameter Combinations in Distributional Semantic Models
Alicia Krebs
|
Denis Paperno
Leveraging VerbNet to build Corpus-Specific Verb Clusters
Daniel Peterson
|
Jordan Boyd-Graber
|
Martha Palmer
|
Daisuke Kawahara
Adding Context to Semantic Data-Driven Paraphrasing
Vered Shwartz
|
Ido Dagan
So-Called Non-Subsective Adjectives
Ellie Pavlick
|
Chris Callison-Burch
Linguistic Style Accommodation in Disagreements
Elise van der Pol
|
Sharon Gieske
|
Raquel Fernández
Unsupervised Text Segmentation Using Semantic Relatedness Graphs
Goran Glavaš
|
Federico Nanni
|
Simone Paolo Ponzetto
Improving Text-to-Pictograph Translation Through Word Sense Disambiguation
Leen Sevens
|
Gilles Jacobs
|
Vincent Vandeghinste
|
Ineke Schuurman
|
Frank Van Eynde
Taking the best from the Crowd:Learning Question Passage Classification from Noisy Data
Azad Abad
|
Alessandro Moschitti
Orthogonality regularizer for question answering
Chunyang Xiao
|
Guillaume Bouchard
|
Marc Dymetman
|
Claire Gardent
The Role of Modifier and Head Properties in Predicting the Compositionality of English and German Noun-Noun Compounds: A Vector-Space Perspective
Sabine Schulte im Walde
|
Anna Hätty
|
Stefan Bott
Detecting Stance in Tweets And Analyzing its Interaction with Sentiment
Parinaz Sobhani
|
Saif Mohammad
|
Svetlana Kiritchenko
A Study of Suggestions in Opinionated Texts and their Automatic Detection
Sapna Negi
|
Kartik Asooja
|
Shubham Mehrotra
|
Paul Buitelaar
You and me... in a vector space: modelling individual speakers with distributional semantics
Aurélie Herbelot
|
Behrang QasemiZadeh
Random Positive-Only Projections: PPMI-Enabled Incremental Semantic Space Construction
Behrang QasemiZadeh
|
Laura Kallmeyer
A Compositional-Distributional Semantic Model for Searching Complex Entity Categories
Juliano Efson Sales
|
André Freitas
|
Brian Davis
|
Siegfried Handschuh
Approximating Givenness in Content Assessment through Distributional Semantics
Ramon Ziai
|
Kordula De Kuthy
|
Detmar Meurers
Learning Embeddings to lexicalise RDF Properties
Laura Perez-Beltrachini
|
Claire Gardent