Lexical and Computational Semantics and Semantic Evaluation (formerly Workshop on Sense Evaluation) (2016)

1 volumes · 212 papers (100% with DOI) · 602 unique authors (36% verified with ORCID)

Volumes

Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016) SemEval 213 papers

bib (full) Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)

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SemEval-2016 Task 4: Sentiment Analysis in Twitter
Preslav Nakov | Alan Ritter | Sara Rosenthal | Fabrizio Sebastiani | Veselin Stoyanov

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SemEval-2016 Task 6: Detecting Stance in Tweets
Saif Mohammad | Svetlana Kiritchenko | Parinaz Sobhani | Xiaodan Zhu | Colin Cherry

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SemEval-2016 Task 7: Determining Sentiment Intensity of English and Arabic Phrases
Svetlana Kiritchenko | Saif Mohammad | Mohammad Salameh

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CUFE at SemEval-2016 Task 4: A Gated Recurrent Model for Sentiment Classification
Mahmoud Nabil | Amir Atyia | Mohamed Aly

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QCRI at SemEval-2016 Task 4: Probabilistic Methods for Binary and Ordinal Quantification
Giovanni Da San Martino | Wei Gao | Fabrizio Sebastiani

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SteM at SemEval-2016 Task 4: Applying Active Learning to Improve Sentiment Classification
Stefan Räbiger | Mishal Kazmi | Yücel Saygın | Peter Schüller | Myra Spiliopoulou

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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

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TwiSE at SemEval-2016 Task 4: Twitter Sentiment Classification
Georgios Balikas | Massih-Reza Amini

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ISTI-CNR at SemEval-2016 Task 4: Quantification on an Ordinal Scale
Andrea Esuli

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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

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thecerealkiller at SemEval-2016 Task 4: Deep Learning based System for Classifying Sentiment of Tweets on Two Point Scale
Vikrant Yadav

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NTNUSentEval at SemEval-2016 Task 4: Combining General Classifiers for Fast Twitter Sentiment Analysis
Brage Ekroll Jahren | Valerij Fredriksen | Björn Gambäck | Lars Bungum

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UDLAP at SemEval-2016 Task 4: Sentiment Quantification Using a Graph Based Representation
Esteban Castillo | Ofelia Cervantes | Darnes Vilariño | David Báez

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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

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Aicyber at SemEval-2016 Task 4: i-vector based sentence representation
Steven Du | Xi Zhang

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PUT at SemEval-2016 Task 4: The ABC of Twitter Sentiment Analysis
Mateusz Lango | Dariusz Brzezinski | Jerzy Stefanowski

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mib at SemEval-2016 Task 4a: Exploiting lexicon based features for Sentiment Analysis in Twitter
Vittoria Cozza | Marinella Petrocchi

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MDSENT at SemEval-2016 Task 4: A Supervised System for Message Polarity Classification
Hang Gao | Tim Oates

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CICBUAPnlp at SemEval-2016 Task 4-A: Discovering Twitter Polarity using Enhanced Embeddings
Helena Gomez | Darnes Vilariño | Grigori Sidorov | David Pinto Avendaño

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Finki at SemEval-2016 Task 4: Deep Learning Architecture for Twitter Sentiment Analysis
Dario Stojanovski | Gjorgji Strezoski | Gjorgji Madjarov | Ivica Dimitrovski

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UofL at SemEval-2016 Task 4: Multi Domain word2vec for Twitter Sentiment Classification
Omar Abdelwahab | Adel Elmaghraby

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NRU-HSE at SemEval-2016 Task 4: Comparative Analysis of Two Iterative Methods Using Quantification Library
Nikolay Karpov | Alexander Porshnev | Kirill Rudakov

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INSIGHT-1 at SemEval-2016 Task 4: Convolutional Neural Networks for Sentiment Classification and Quantification
Sebastian Ruder | Parsa Ghaffari | John G. Breslin

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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

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SentiSys at SemEval-2016 Task 4: Feature-Based System for Sentiment Analysis in Twitter
Hussam Hamdan

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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

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SENSEI-LIF at SemEval-2016 Task 4: Polarity embedding fusion for robust sentiment analysis
Mickael Rouvier | Benoit Favre

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DiegoLab16 at SemEval-2016 Task 4: Sentiment Analysis in Twitter using Centroids, Clusters, and Sentiment Lexicons
Abeed Sarker | Graciela Gonzalez

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VCU-TSA at Semeval-2016 Task 4: Sentiment Analysis in Twitter
Gerard Briones | Kasun Amarasinghe | Bridget McInnes

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UniPI at SemEval-2016 Task 4: Convolutional Neural Networks for Sentiment Classification
Giuseppe Attardi | Daniele Sartiano

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IIP at SemEval-2016 Task 4: Prioritizing Classes in Ensemble Classification for Sentiment Analysis of Tweets
Jasper Friedrichs

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PotTS at SemEval-2016 Task 4: Sentiment Analysis of Twitter Using Character-level Convolutional Neural Networks.
Uladzimir Sidarenka

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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

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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

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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

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OPAL at SemEval-2016 Task 4: the Challenge of Porting a Sentiment Analysis System to the “Real” World
Alexandra Balahur

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Know-Center at SemEval-2016 Task 5: Using Word Vectors with Typed Dependencies for Opinion Target Expression Extraction
Stefan Falk | Andi Rexha | Roman Kern

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NileTMRG at SemEval-2016 Task 5: Deep Convolutional Neural Networks for Aspect Category and Sentiment Extraction
Talaat Khalil | Samhaa R. El-Beltagy

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XRCE at SemEval-2016 Task 5: Feedbacked Ensemble Modeling on Syntactico-Semantic Knowledge for Aspect Based Sentiment Analysis
Caroline Brun | Julien Perez | Claude Roux

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NLANGP at SemEval-2016 Task 5: Improving Aspect Based Sentiment Analysis using Neural Network Features
Zhiqiang Toh | Jian Su

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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

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IHS-RD-Belarus at SemEval-2016 Task 5: Detecting Sentiment Polarity Using the Heatmap of Sentence
Maryna Chernyshevich

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BUTknot at SemEval-2016 Task 5: Supervised Machine Learning with Term Substitution Approach in Aspect Category Detection
Jakub Macháček

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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

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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

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AKTSKI at SemEval-2016 Task 5: Aspect Based Sentiment Analysis for Consumer Reviews
Shubham Pateria | Prafulla Choubey

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MayAnd at SemEval-2016 Task 5: Syntactic and word2vec-based approach to aspect-based polarity detection in Russian
Vladimir Mayorov | Ivan Andrianov

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INSIGHT-1 at SemEval-2016 Task 5: Deep Learning for Multilingual Aspect-based Sentiment Analysis
Sebastian Ruder | Parsa Ghaffari | John G. Breslin

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TGB at SemEval-2016 Task 5: Multi-Lingual Constraint System for Aspect Based Sentiment Analysis
Fatih Samet Çetin | Ezgi Yıldırım | Can Özbey | Gülşen Eryiğit

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UWB at SemEval-2016 Task 5: Aspect Based Sentiment Analysis
Tomáš Hercig | Tomáš Brychcín | Lukáš Svoboda | Michal Konkol

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SentiSys at SemEval-2016 Task 5: Opinion Target Extraction and Sentiment Polarity Detection
Hussam Hamdan

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COMMIT at SemEval-2016 Task 5: Sentiment Analysis with Rhetorical Structure Theory
Kim Schouten | Flavius Frasincar

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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

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UFAL at SemEval-2016 Task 5: Recurrent Neural Networks for Sentence Classification
Aleš Tamchyna | Kateřina Veselovská

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UWaterloo at SemEval-2016 Task 5: Minimally Supervised Approaches to Aspect-Based Sentiment Analysis
Olga Vechtomova | Anni He

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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

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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

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USFD at SemEval-2016 Task 6: Any-Target Stance Detection on Twitter with Autoencoders
Isabelle Augenstein | Andreas Vlachos | Kalina Bontcheva

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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

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UWB at SemEval-2016 Task 6: Stance Detection
Peter Krejzl | Josef Steinberger

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DeepStance at SemEval-2016 Task 6: Detecting Stance in Tweets Using Character and Word-Level CNNs
Prashanth Vijayaraghavan | Ivan Sysoev | Soroush Vosoughi | Deb Roy

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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

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ltl.uni-due at SemEval-2016 Task 6: Stance Detection in Social Media Using Stacked Classifiers
Michael Wojatzki | Torsten Zesch

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CU-GWU Perspective at SemEval-2016 Task 6: Ideological Stance Detection in Informal Text
Heba Elfardy | Mona Diab

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JU_NLP at SemEval-2016 Task 6: Detecting Stance in Tweets using Support Vector Machines
Braja Gopal Patra | Dipankar Das | Sivaji Bandyopadhyay

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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

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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

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MITRE at SemEval-2016 Task 6: Transfer Learning for Stance Detection
Guido Zarrella | Amy Marsh

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LSIS at SemEval-2016 Task 7: Using Web Search Engines for English and Arabic Unsupervised Sentiment Intensity Prediction
Amal Htait | Sebastien Fournier | Patrice Bellot

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iLab-Edinburgh at SemEval-2016 Task 7: A Hybrid Approach for Determining Sentiment Intensity of Arabic Twitter Phrases
Eshrag Refaee | Verena Rieser

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UWB at SemEval-2016 Task 7: Novel Method for Automatic Sentiment Intensity Determination
Ladislav Lenc | Pavel Král | Václav Rajtmajer

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NileTMRG at SemEval-2016 Task 7: Deriving Prior Polarities for Arabic Sentiment Terms
Samhaa R. El-Beltagy

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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 10: Detecting Minimal Semantic Units and their Meanings (DiMSUM)
Nathan Schneider | Dirk Hovy | Anders Johannsen | Marine Carpuat

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SemEval 2016 Task 11: Complex Word Identification
Gustavo Paetzold | Lucia Specia

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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

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VRep at SemEval-2016 Task 1 and Task 2: A System for Interpretable Semantic Similarity
Sam Henry | Allison Sands

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UTA DLNLP at SemEval-2016 Task 1: Semantic Textual Similarity: A Unified Framework for Semantic Processing and Evaluation
Peng Li | Heng Huang

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UWB at SemEval-2016 Task 1: Semantic Textual Similarity using Lexical, Syntactic, and Semantic Information
Tomáš Brychcín | Lukáš Svoboda

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HHU at SemEval-2016 Task 1: Multiple Approaches to Measuring Semantic Textual Similarity
Matthias Liebeck | Philipp Pollack | Pashutan Modaresi | Stefan Conrad

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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

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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

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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

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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

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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

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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

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DLS@CU at SemEval-2016 Task 1: Supervised Models of Sentence Similarity
Md Arafat Sultan | Steven Bethard | Tamara Sumner

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DCU-SEManiacs at SemEval-2016 Task 1: Synthetic Paragram Embeddings for Semantic Textual Similarity
Chris Hokamp | Piyush Arora

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GWU NLP at SemEval-2016 Shared Task 1: Matrix Factorization for Crosslingual STS
Hanan Aldarmaki | Mona Diab

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CNRC at SemEval-2016 Task 1: Experiments in Crosslingual Semantic Textual Similarity
Chi-kiu Lo | Cyril Goutte | Michel Simard

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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

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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

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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

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RICOH at SemEval-2016 Task 1: IR-based Semantic Textual Similarity Estimation
Hideo Itoh

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IHS-RD-Belarus at SemEval-2016 Task 1: Multistage Approach for Measuring Semantic Similarity
Maryna Beliuha | Maryna Chernyshevich

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JUNITMZ at SemEval-2016 Task 1: Identifying Semantic Similarity Using Levenshtein Ratio
Sandip Sarkar | Dipankar Das | Partha Pakray | Alexander Gelbukh

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Amrita_CEN at SemEval-2016 Task 1: Semantic Relation from Word Embeddings in Higher Dimension
Barathi Ganesh HB | Anand Kumar M | Soman KP

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NUIG-UNLP at SemEval-2016 Task 1: Soft Alignment and Deep Learning for Semantic Textual Similarity
John P. McCrae | Kartik Asooja | Nitish Aggarwal | Paul Buitelaar

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NORMAS at SemEval-2016 Task 1: SEMSIM: A Multi-Feature Approach to Semantic Text Similarity
Kolawole John Adebayo | Luigi Di Caro | Guido Boella

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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

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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

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ASOBEK at SemEval-2016 Task 1: Sentence Representation with Character N-gram Embeddings for Semantic Textual Similarity
Asli Eyecioglu | Bill Keller

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SimiHawk at SemEval-2016 Task 1: A Deep Ensemble System for Semantic Textual Similarity
Peter Potash | William Boag | Alexey Romanov | Vasili Ramanishka | Anna Rumshisky

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SERGIOJIMENEZ at SemEval-2016 Task 1: Effectively Combining Paraphrase Database, String Matching, WordNet, and Word Embedding for Semantic Textual Similarity
Sergio Jimenez

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RTM at SemEval-2016 Task 1: Predicting Semantic Similarity with Referential Translation Machines and Related Statistics
Ergun Biçici

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DalGTM at SemEval-2016 Task 1: Importance-Aware Compositional Approach to Short Text Similarity
Jie Mei | Aminul Islam | Evangelos Milios

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iUBC at SemEval-2016 Task 2: RNNs and LSTMs for interpretable STS
Iñigo Lopez-Gazpio | Eneko Agirre | Montse Maritxalar

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Rev at SemEval-2016 Task 2: Aligning Chunks by Lexical, Part of Speech and Semantic Equivalence
Ping Tan | Karin Verspoor | Timothy Miller

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FBK-HLT-NLP at SemEval-2016 Task 2: A Multitask, Deep Learning Approach for Interpretable Semantic Textual Similarity
Simone Magnolini | Anna Feltracco | Bernardo Magnini

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IISCNLP at SemEval-2016 Task 2: Interpretable STS with ILP based Multiple Chunk Aligner
Lavanya Tekumalla | Sharmistha Jat

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VENSESEVAL at Semeval-2016 Task 2 iSTS - with a full-fledged rule-based approach
Rodolfo Delmonte

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UWB at SemEval-2016 Task 2: Interpretable Semantic Textual Similarity with Distributional Semantics for Chunks
Miloslav Konopík | Ondřej Pražák | David Steinberger | Tomáš Brychcín

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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

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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

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

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ICL00 at SemEval-2016 Task 3: Translation-Based Method for CQA System
Yunfang Wu | Minghua Zhang

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Overfitting at SemEval-2016 Task 3: Detecting Semantically Similar Questions in Community Question Answering Forums with Word Embeddings
Hujie Wang | Pascal Poupart

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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

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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

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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

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MTE-NN at SemEval-2016 Task 3: Can Machine Translation Evaluation Help Community Question Answering?
Francisco Guzmán | Preslav Nakov | Lluís Màrquez

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ITNLP-AiKF at SemEval-2016 Task 3 a quesiton answering system using community QA repository
Chang’e Jia

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UFRGS&LIF at SemEval-2016 Task 10: Rule-Based MWE Identification and Predominant-Supersense Tagging
Silvio Cordeiro | Carlos Ramisch | Aline Villavicencio

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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

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UTU at SemEval-2016 Task 10: Binary Classification for Expression Detection (BCED)
Jari Björne | Tapio Salakoski

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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

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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

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VectorWeavers at SemEval-2016 Task 10: From Incremental Meaning to Semantic Unit (phrase by phrase)
Andreas Scherbakov | Ekaterina Vylomova | Fei Liu | Timothy Baldwin

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PLUJAGH at SemEval-2016 Task 11: Simple System for Complex Word Identification
Krzysztof Wróbel

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USAAR at SemEval-2016 Task 11: Complex Word Identification with Sense Entropy and Sentence Perplexity
José Manuel Martínez Martínez | Liling Tan

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Sensible at SemEval-2016 Task 11: Neural Nonsense Mangled in Ensemble Mess
Gillin Nat

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SV000gg at SemEval-2016 Task 11: Heavy Gauge Complex Word Identification with System Voting
Gustavo Paetzold | Lucia Specia

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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

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CLaC at SemEval-2016 Task 11: Exploring linguistic and psycho-linguistic Features for Complex Word Identification
Elnaz Davoodi | Leila Kosseim

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JU_NLP at SemEval-2016 Task 11: Identifying Complex Words in a Sentence
Niloy Mukherjee | Braja Gopal Patra | Dipankar Das | Sivaji Bandyopadhyay

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MAZA at SemEval-2016 Task 11: Detecting Lexical Complexity Using a Decision Stump Meta-Classifier
Shervin Malmasi | Marcos Zampieri

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LTG at SemEval-2016 Task 11: Complex Word Identification with Classifier Ensembles
Shervin Malmasi | Mark Dras | Marcos Zampieri

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MacSaar at SemEval-2016 Task 11: Zipfian and Character Features for ComplexWord Identification
Marcos Zampieri | Liling Tan | Josef van Genabith

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Garuda & Bhasha at SemEval-2016 Task 11: Complex Word Identification Using Aggregated Learning Models
Prafulla Choubey | Shubham Pateria

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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

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IIIT at SemEval-2016 Task 11: Complex Word Identification using Nearest Centroid Classification
Ashish Palakurthi | Radhika Mamidi

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AmritaCEN at SemEval-2016 Task 11: Complex Word Identification using Word Embedding
Sanjay S.P | Anand Kumar M | Soman K P

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CoastalCPH at SemEval-2016 Task 11: The importance of designing your Neural Networks right
Joachim Bingel | Natalie Schluter | Héctor Martínez Alonso

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HMC at SemEval-2016 Task 11: Identifying Complex Words Using Depth-limited Decision Trees
Maury Quijada | Julie Medero

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UWB at SemEval-2016 Task 11: Exploring Features for Complex Word Identification
Michal Konkol

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AI-KU at SemEval-2016 Task 11: Word Embeddings and Substring Features for Complex Word Identification
Onur Kuru

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Pomona at SemEval-2016 Task 11: Predicting Word Complexity Based on Corpus Frequency
David Kauchak

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SemEval-2016 Task 8: Meaning Representation Parsing
Jonathan May

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SemEval-2016 Task 9: Chinese Semantic Dependency Parsing
Wanxiang Che | Yanqiu Shao | Ting Liu | Yu Ding

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SemEval-2016 Task 13: Taxonomy Extraction Evaluation (TExEval-2)
Georgeta Bordea | Els Lefever | Paul Buitelaar

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SemEval-2016 Task 14: Semantic Taxonomy Enrichment
David Jurgens | Mohammad Taher Pilehvar

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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

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Inspire at SemEval-2016 Task 2: Interpretable Semantic Textual Similarity Alignment based on Answer Set Programming
Mishal Kazmi | Peter Schüller

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KeLP at SemEval-2016 Task 3: Learning Semantic Relations between Questions and Answers
Simone Filice | Danilo Croce | Alessandro Moschitti | Roberto Basili

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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

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LIMSI-COT at SemEval-2016 Task 12: Temporal relation identification using a pipeline of classifiers
Julien Tourille | Olivier Ferret | Aurélie Névéol | Xavier Tannier

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RIGA at SemEval-2016 Task 8: Impact of Smatch Extensions and Character-Level Neural Translation on AMR Parsing Accuracy
Guntis Barzdins | Didzis Gosko

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DynamicPower at SemEval-2016 Task 8: Processing syntactic parse trees with a Dynamic Semantics core
Alastair Butler

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M2L at SemEval-2016 Task 8: AMR Parsing with Neural Networks
Yevgeniy Puzikov | Daisuke Kawahara | Sadao Kurohashi

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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

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UCL+Sheffield at SemEval-2016 Task 8: Imitation learning for AMR parsing with an alpha-bound
James Goodman | Andreas Vlachos | Jason Naradowsky

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CAMR at SemEval-2016 Task 8: An Extended Transition-based AMR Parser
Chuan Wang | Sameer Pradhan | Xiaoman Pan | Heng Ji | Nianwen Xue

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The Meaning Factory at SemEval-2016 Task 8: Producing AMRs with Boxer
Johannes Bjerva | Johan Bos | Hessel Haagsma

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UofR at SemEval-2016 Task 8: Learning Synchronous Hyperedge Replacement Grammar for AMR Parsing
Xiaochang Peng | Daniel Gildea

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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

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CU-NLP at SemEval-2016 Task 8: AMR Parsing using LSTM-based Recurrent Neural Networks
William Foland | James H. Martin

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CMU at SemEval-2016 Task 8: Graph-based AMR Parsing with Infinite Ramp Loss
Jeffrey Flanigan | Chris Dyer | Noah A. Smith | Jaime Carbonell

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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

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OCLSP at SemEval-2016 Task 9: Multilayered LSTM as a Neural Semantic Dependency Parser
Lifeng Jin | Manjuan Duan | William Schuler

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OSU_CHGCG at SemEval-2016 Task 9 : Chinese Semantic Dependency Parsing with Generalized Categorial Grammar
Manjuan Duan | Lifeng Jin | William Schuler

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LIMSI at SemEval-2016 Task 12: machine-learning and temporal information to identify clinical events and time expressions
Cyril Grouin | Véronique Moriceau

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Hitachi at SemEval-2016 Task 12: A Hybrid Approach for Temporal Information Extraction from Clinical Notes
Sarath P R | Manikandan R | Yoshiki Niwa

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CDE-IIITH at SemEval-2016 Task 12: Extraction of Temporal Information from Clinical documents using Machine Learning techniques
Veera Raghavendra Chikka

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VUACLTL at SemEval 2016 Task 12: A CRF Pipeline to Clinical TempEval
Tommaso Caselli | Roser Morante

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GUIR at SemEval-2016 task 12: Temporal Information Processing for Clinical Narratives
Arman Cohan | Kevin Meurer | Nazli Goharian

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UtahBMI at SemEval-2016 Task 12: Extracting Temporal Information from Clinical Text
Abdulrahman Khalifa | Sumithra Velupillai | Stephane Meystre

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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

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Brundlefly at SemEval-2016 Task 12: Recurrent Neural Networks vs. Joint Inference for Clinical Temporal Information Extraction
Jason Fries

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KULeuven-LIIR at SemEval 2016 Task 12: Detecting Narrative Containment in Clinical Records
Artuur Leeuwenberg | Marie-Francine Moens

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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

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NUIG-UNLP at SemEval-2016 Task 13: A Simple Word Embedding-based Approach for Taxonomy Extraction
Joel Pocostales

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USAAR at SemEval-2016 Task 13: Hyponym Endocentricity
Liling Tan | Francis Bond | Josef van Genabith

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JUNLP at SemEval-2016 Task 13: A Language Independent Approach for Hypernym Identification
Promita Maitra | Dipankar Das

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QASSIT at SemEval-2016 Task 13: On the integration of Semantic Vectors in Pretopological Spaces for Lexical Taxonomy Acquisition
Guillaume Cleuziou | Jose G. Moreno

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Duluth at SemEval 2016 Task 14: Extending Gloss Overlaps to Enrich Semantic Taxonomies
Ted Pedersen

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TALN at SemEval-2016 Task 14: Semantic Taxonomy Enrichment Via Sense-Based Embeddings
Luis Espinosa-Anke | Francesco Ronzano | Horacio Saggion

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MSejrKu at SemEval-2016 Task 14: Taxonomy Enrichment by Evidence Ranking
Michael Schlichtkrull | Héctor Martínez Alonso

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Deftor at SemEval-2016 Task 14: Taxonomy enrichment using definition vectors
Hristo Tanev | Agata Rotondi

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UMNDuluth at SemEval-2016 Task 14: WordNet’s Missing Lemmas
Jon Rusert | Ted Pedersen

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VCU at Semeval-2016 Task 14: Evaluating definitional-based similarity measure for semantic taxonomy enrichment
Bridget McInnes