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SemEval 2016

213 papers

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 García Flores, Davide Buscaldi

NORMAS at SemEval-2016 Task 1: SEMSIM: A Multi-Feature Approach to Semantic Text Similarity

Kolawole Adebayo, Luigi Di Caro, Guido Boella

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

Amrita_CEN at SemEval-2016 Task 1: Semantic Relation from Word Embeddings in Higher Dimension

Barathi Ganesh Hb, Anand Kumar M, Soman Kp

JUNITMZ at SemEval-2016 Task 1: Identifying Semantic Similarity Using Levenshtein Ratio

Sandip Sarkar, Dipankar Das, Partha Pakray, Alexander Gelbukh

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

Maryna Beliuha, Maryna Chernyshevich

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

Hideo Itoh

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

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

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

CNRC at SemEval-2016 Task 1: Experiments in Crosslingual Semantic Textual Similarity

Chi-kiu Lo, Cyril Goutte, Michel Simard

GWU NLP at SemEval-2016 Shared Task 1: Matrix Factorization for Crosslingual STS

Hanan Aldarmaki, Mona Diab

DCU-SEManiacs at SemEval-2016 Task 1: Synthetic Paragram Embeddings for Semantic Textual Similarity

Chris Hokamp, Piyush Arora

DLS@CU at SemEval-2016 Task 1: Supervised Models of Sentence Similarity

Md Arafat Sultan, Steven Bethard, Tamara Sumner

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

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

WOLVESAAR at SemEval-2016 Task 1: Replicating the Success of Monolingual Word Alignment and Neural Embeddings for Semantic Textual Similarity

Hannah Béchara, Rohit Gupta, Liling Tan, Constantin Orasan, Ruslan Mitkov

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

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

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

USFD at SemEval-2016 Task 1: Putting different State-of-the-Arts into a Box

Ahmet Aker, Frédéric Blain, Andres Duque, Marina Fomicheva, Jurica Ševa

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

HHU at SemEval-2016 Task 1: Multiple Approaches to Measuring Semantic Textual Similarity

Matthias Liebeck, Philipp Pollack, Pashutan Modaresi, Stefan Conrad

UWB at SemEval-2016 Task 1: Semantic Textual Similarity using Lexical, Syntactic, and Semantic Information

Tomáš Brychcín, Lukáš Svoboda

UTA DLNLP at SemEval-2016 Task 1: Semantic Textual Similarity: A Unified Framework for Semantic Processing and Evaluation

Peng Li, Heng Huang

VRep at SemEval-2016 Task 1 and Task 2: A System for Interpretable Semantic Similarity

Sam Henry, Allison Sands

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

SemEval 2016 Task 11: Complex Word Identification

Gustavo Paetzold, Lucia Specia

SemEval-2016 Task 10: Detecting Minimal Semantic Units and their Meanings (DiMSUM)

Nathan Schneider, Dirk Hovy, Anders Johannsen, Marine Carpuat

SemEval-2016 Task 3: Community Question Answering

Preslav Nakov, Lluis Marquez, Alessandro Moschitti, Walid Magdy, Hamdy Mubarak

SemEval-2016 Task 2: Interpretable Semantic Textual Similarity

Eneko Agirre, Aitor Gonzalez-Agirre, Iñigo Lopez-Gazpio, Montse Maritxalar, German Rigau

SemEval-2016 Task 1: Semantic Textual Similarity, Monolingual and Cross-Lingual Evaluation

Eneko Agirre, Carmen Banea, Daniel Cer, Mona Diab, Aitor Gonzalez-Agirre

ECNU at SemEval-2016 Task 7: An Enhanced Supervised Learning Method for Lexicon Sentiment Intensity Ranking

Feixiang Wang, Zhihua Zhang, Man Lan

NileTMRG at SemEval-2016 Task 7: Deriving Prior Polarities for Arabic Sentiment Terms

Samhaa R. El-Beltagy

UWB at SemEval-2016 Task 7: Novel Method for Automatic Sentiment Intensity Determination

Ladislav Lenc, Pavel Kral, Václav Rajtmajer

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

Eshrag Refaee, Verena Rieser

LSIS at SemEval-2016 Task 7: Using Web Search Engines for English and Arabic Unsupervised Sentiment Intensity Prediction

Amal Htait, Sébastien Fournier, Patrice Bellot

TakeLab at SemEval-2016 Task 6: Stance Classification in Tweets Using a Genetic Algorithm Based Ensemble

Martin Tutek, Ivan Sekulic, Paula Gombar, Ivan Paljak, Filip Čulinović

MITRE at SemEval-2016 Task 6: Transfer Learning for Stance Detection

Guido Zarrella, Amy Marsh

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

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

JU_NLP at SemEval-2016 Task 6: Detecting Stance in Tweets using Support Vector Machines

Braja Gopal Patra, Dipankar Das, Sivaji Bandyopadhyay

CU-GWU Perspective at SemEval-2016 Task 6: Ideological Stance Detection in Informal Text

Heba Elfardy, Mona Diab

ltl.uni-due at SemEval-2016 Task 6: Stance Detection in Social Media Using Stacked Classifiers

Michael Wojatzki, Torsten Zesch

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

DeepStance at SemEval-2016 Task 6: Detecting Stance in Tweets Using Character and Word-Level CNNs

Prashanth Vijayaraghavan, Ivan Sysoev, Soroush Vosoughi, Deb Roy

UWB at SemEval-2016 Task 6: Stance Detection

Peter Krejzl, Josef Steinberger

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

IUCL at SemEval-2016 Task 6: An Ensemble Model for Stance Detection in Twitter

Can Liu, Wen Li, Bradford Demarest, Yue Chen, Sara Couture

USFD at SemEval-2016 Task 6: Any-Target Stance Detection on Twitter with Autoencoders

Isabelle Augenstein, Andreas Vlachos, Kalina Bontcheva

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

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

UWaterloo at SemEval-2016 Task 5: Minimally Supervised Approaches to Aspect-Based Sentiment Analysis

Olga Vechtomova, Anni He

UFAL at SemEval-2016 Task 5: Recurrent Neural Networks for Sentence Classification

Aleš Tamchyna, Kateřina Veselovská

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

COMMIT at SemEval-2016 Task 5: Sentiment Analysis with Rhetorical Structure Theory

Kim Schouten, Flavius Frasincar

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

Hussam Hamdan

UWB at SemEval-2016 Task 5: Aspect Based Sentiment Analysis

Tomáš Hercig, Tomáš Brychcín, Lukáš Svoboda, Michal Konkol

TGB at SemEval-2016 Task 5: Multi-Lingual Constraint System for Aspect Based Sentiment Analysis

Fatih Samet Çetin, Ezgi Yıldırım, Can Özbey, Gülşen Eryiǧit

INSIGHT-1 at SemEval-2016 Task 5: Deep Learning for Multilingual Aspect-based Sentiment Analysis

Sebastian Ruder, Parsa Ghaffari, John G. Breslin

MayAnd at SemEval-2016 Task 5: Syntactic and word2vec-based approach to aspect-based polarity detection in Russian

Vladimir Mayorov, Ivan Andrianov

AKTSKI at SemEval-2016 Task 5: Aspect Based Sentiment Analysis for Consumer Reviews

Shubham Pateria, Prafulla Choubey

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

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

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

Jakub Macháček

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

Maryna Chernyshevich

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

NLANGP at SemEval-2016 Task 5: Improving Aspect Based Sentiment Analysis using Neural Network Features

Zhiqiang Toh, Jian Su

XRCE at SemEval-2016 Task 5: Feedbacked Ensemble Modeling on Syntactico-Semantic Knowledge for Aspect Based Sentiment Analysis

Caroline Brun, Julien Perez, Claude Roux

NileTMRG at SemEval-2016 Task 5: Deep Convolutional Neural Networks for Aspect Category and Sentiment Extraction

Talaat Khalil, Samhaa R. El-Beltagy

Know-Center at SemEval-2016 Task 5: Using Word Vectors with Typed Dependencies for Opinion Target Expression Extraction

Stefan Falk, Andi Rexha, Roman Kern

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

Alexandra Balahur

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

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

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 Trandabat

SentimentalITsts at SemEval-2016 Task 4: building a Twitter sentiment analyzer in your backyard

Cosmin Florean, Oana Bejenaru, Eduard Apostol, Octavian Ciobanu, Adrian Iftene

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

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

Uladzimir Sidarenka

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

Jasper Friedrichs

UniPI at SemEval-2016 Task 4: Convolutional Neural Networks for Sentiment Classification

Giuseppe Attardi, Daniele Sartiano

VCU-TSA at Semeval-2016 Task 4: Sentiment Analysis in Twitter

Gerard Briones, Kasun Amarasinghe, Bridget McInnes

DiegoLab16 at SemEval-2016 Task 4: Sentiment Analysis in Twitter using Centroids, Clusters, and Sentiment Lexicons

Abeed Sarker, Graciela Gonzalez

SENSEI-LIF at SemEval-2016 Task 4: Polarity embedding fusion for robust sentiment analysis

Mickael Rouvier, Benoit Favre

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

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

Hussam Hamdan

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

INSIGHT-1 at SemEval-2016 Task 4: Convolutional Neural Networks for Sentiment Classification and Quantification

Sebastian Ruder, Parsa Ghaffari, John G. Breslin

NRU-HSE at SemEval-2016 Task 4: Comparative Analysis of Two Iterative Methods Using Quantification Library

Nikolay Karpov, Alexander Porshnev, Kirill Rudakov

UofL at SemEval-2016 Task 4: Multi Domain word2vec for Twitter Sentiment Classification

Omar Abdelwahab, Adel Elmaghraby

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

Finki at SemEval-2016 Task 4: Deep Learning Architecture for Twitter Sentiment Analysis

Dario Stojanovski, Gjorgji Strezoski, Gjorgji Madjarov, Ivica Dimitrovski

CICBUAPnlp at SemEval-2016 Task 4-A: Discovering Twitter Polarity using Enhanced Embeddings

Helena Gomez, Darnes Vilariño, Grigori Sidorov, David Pinto Avendaño

MDSENT at SemEval-2016 Task 4: A Supervised System for Message Polarity Classification

Hang Gao, Tim Oates

mib at SemEval-2016 Task 4a: Exploiting lexicon based features for Sentiment Analysis in Twitter

Vittoria Cozza, Marinella Petrocchi

PUT at SemEval-2016 Task 4: The ABC of Twitter Sentiment Analysis

Mateusz Lango, Dariusz Brzezinski, Jerzy Stefanowski

Aicyber at SemEval-2016 Task 4: i-vector based sentence representation

Steven Du, Xi Zhang

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

UDLAP at SemEval-2016 Task 4: Sentiment Quantification Using a Graph Based Representation

Esteban Castillo, Ofelia Cervantes, Darnes Vilariño, David Báez

NTNUSentEval at SemEval-2016 Task 4: Combining General Classifiers for Fast Twitter Sentiment Analysis

Brage Ekroll Jahren, Valerij Fredriksen, Björn Gambäck, Lars Bungum

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

Vikrant Yadav