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Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016)
Proceedings of the Fifth Joint Conference on Lexical and Computational Semantics

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

S16-1000: Front Matter

S16-1001 [bib]: Preslav Nakov; Alan Ritter; Sara Rosenthal; Fabrizio Sebastiani; Veselin Stoyanov
SemEval-2016 Task 4: Sentiment Analysis in Twitter

S16-1002 [bib]: Maria Pontiki; Dimitris Galanis; Haris Papageorgiou; Ion Androutsopoulos; Suresh Manandhar; Mohammad AL-Smadi; Mahmoud Al-Ayyoub; Yanyan Zhao; Bing Qin; Orphee De Clercq; Veronique Hoste; Marianna Apidianaki; Xavier Tannier; Natalia Loukachevitch; Evgeniy Kotelnikov; Núria Bel; Salud María Jiménez-Zafra; Gülşen Eryiğit
SemEval-2016 Task 5: Aspect Based Sentiment Analysis

S16-1003 [bib]: Saif Mohammad; Svetlana Kiritchenko; Parinaz Sobhani; Xiaodan Zhu; Colin Cherry
SemEval-2016 Task 6: Detecting Stance in Tweets

S16-1004 [bib]: Svetlana Kiritchenko; Saif Mohammad; Mohammad Salameh
SemEval-2016 Task 7: Determining Sentiment Intensity of English and Arabic Phrases

S16-1005 [bib]: Mahmoud Nabil; Amir Atyia; Mohamed Aly
CUFE at SemEval-2016 Task 4: A Gated Recurrent Model for Sentiment Classification

S16-1006 [bib]: Giovanni Da San Martino; Wei Gao; Fabrizio Sebastiani
QCRI at SemEval-2016 Task 4: Probabilistic Methods for Binary and Ordinal Quantification

S16-1007 [bib]: Stefan Räbiger; Mishal Kazmi; Yücel Saygın; Peter Schüller; Myra Spiliopoulou
SteM at SemEval-2016 Task 4: Applying Active Learning to Improve Sentiment Classification

S16-1008 [bib]: Zhengchen Zhang; Chen Zhang; wu fuxiang; Dongyan Huang; Weisi Lin; Minghui Dong
I2RNTU at SemEval-2016 Task 4: Classifier Fusion for Polarity Classification in Twitter

S16-1009 [bib]: David Vilares; Yerai Doval; Miguel A. Alonso; Carlos Gómez-Rodríguez
LyS at SemEval-2016 Task 4: Exploiting Neural Activation Values for Twitter Sentiment Classification and Quantification

S16-1010 [bib]: Georgios Balikas; Massih-Reza Amini
TwiSE at SemEval-2016 Task 4: Twitter Sentiment Classification

S16-1011 [bib]: Andrea Esuli
ISTI-CNR at SemEval-2016 Task 4: Quantification on an Ordinal Scale

S16-1012 [bib]: Stavros Giorgis; Apostolos Rousas; John Pavlopoulos; Prodromos Malakasiotis; Ion Androutsopoulos
aueb.twitter.sentiment at SemEval-2016 Task 4: A Weighted Ensemble of SVMs for Twitter Sentiment Analysis

S16-1013 [bib]: Vikrant Yadav
thecerealkiller at SemEval-2016 Task 4: Deep Learning based System for Classifying Sentiment of Tweets on Two Point Scale

S16-1014 [bib]: Brage Ekroll Jahren; Valerij Fredriksen; Björn Gambäck; Lars Bungum
NTNUSentEval at SemEval-2016 Task 4: Combining General Classifiers for Fast Twitter Sentiment Analysis

S16-1015 [bib]: Esteban Castillo; Ofelia Cervantes; Darnes Vilariño; David Báez
UDLAP at SemEval-2016 Task 4: Sentiment Quantification Using a Graph Based Representation

S16-1016 [bib]: Jonathan Juncal-Martínez; Tamara Àlvarez-López; Milagros Fernández-Gavilanes; Enrique Costa-Montenegro; Francisco Javier González-Castaño
GTI at SemEval-2016 Task 4: Training a Naive Bayes Classifier using Features of an Unsupervised System

S16-1017 [bib]: Steven Du; Xi Zhang
Aicyber at SemEval-2016 Task 4: i-vector based sentence representation

S16-1018 [bib]: Mateusz Lango; Dariusz Brzezinski; Jerzy Stefanowski
PUT at SemEval-2016 Task 4: The ABC of Twitter Sentiment Analysis

S16-1019 [bib]: Vittoria Cozza; Marinella Petrocchi
mib at SemEval-2016 Task 4a: Exploiting lexicon based features for Sentiment Analysis in Twitter

S16-1020 [bib]: Hang Gao; Tim Oates
MDSENT at SemEval-2016 Task 4: A Supervised System for Message Polarity Classification

S16-1021 [bib]: Helena Gomez; Darnes Vilariño; Grigori Sidorov; David Pinto Avendaño
CICBUAPnlp at SemEval-2016 Task 4-A: Discovering Twitter Polarity using Enhanced Embeddings

S16-1022 [bib]: Dario Stojanovski; Gjorgji Strezoski; Gjorgji Madjarov; Ivica Dimitrovski
Finki at SemEval-2016 Task 4: Deep Learning Architecture for Twitter Sentiment Analysis

S16-1023 [bib]: Elisavet Palogiannidi; Athanasia Kolovou; Fenia Christopoulou; Filippos Kokkinos; Elias Iosif; Nikolaos Malandrakis; Haris Papageorgiou; Shrikanth Narayanan; Alexandros Potamianos
Tweester at SemEval-2016 Task 4: Sentiment Analysis in Twitter Using Semantic-Affective Model Adaptation

S16-1024 [bib]: Omar Abdelwahab; Adel Elmaghraby
UofL at SemEval-2016 Task 4: Multi Domain word2vec for Twitter Sentiment Classification

S16-1025 [bib]: Nikolay Karpov; Alexander Porshnev; Kirill Rudakov
NRU-HSE at SemEval-2016 Task 4: Comparative Analysis of Two Iterative Methods Using Quantification Library

S16-1026 [bib]: Sebastian Ruder; Parsa Ghaffari; John G. Breslin
INSIGHT-1 at SemEval-2016 Task 4: Convolutional Neural Networks for Sentiment Classification and Quantification

S16-1027 [bib]: Steven Xu; HuiZhi Liang; Timothy Baldwin
UNIMELB at SemEval-2016 Tasks 4A and 4B: An Ensemble of Neural Networks and a Word2Vec Based Model for Sentiment Classification

S16-1028 [bib]: Hussam Hamdan
SentiSys at SemEval-2016 Task 4: Feature-Based System for Sentiment Analysis in Twitter

S16-1029 [bib]: Victor Martinez Morant; Lluís-F Hurtado; Ferran Pla
DSIC-ELIRF at SemEval-2016 Task 4: Message Polarity Classification in Twitter using a Support Vector Machine Approach

S16-1030 [bib]: Mickael Rouvier; Benoit Favre
SENSEI-LIF at SemEval-2016 Task 4: Polarity embedding fusion for robust sentiment analysis

S16-1031 [bib]: Abeed Sarker; Graciela Gonzalez
DiegoLab16 at SemEval-2016 Task 4: Sentiment Analysis in Twitter using Centroids, Clusters, and Sentiment Lexicons

S16-1032 [bib]: Gerard Briones; Kasun Amarasinghe; Bridget McInnes
VCU-TSA at Semeval-2016 Task 4: Sentiment Analysis in Twitter

S16-1033 [bib]: Giuseppe Attardi; Daniele Sartiano
UniPI at SemEval-2016 Task 4: Convolutional Neural Networks for Sentiment Classification

S16-1034 [bib]: Jasper Friedrichs
IIP at SemEval-2016 Task 4: Prioritizing Classes in Ensemble Classification for Sentiment Analysis of Tweets

S16-1035 [bib]: Uladzimir Sidarenka
PotTS at SemEval-2016 Task 4: Sentiment Analysis of Twitter Using Character-level Convolutional Neural Networks.

S16-1036 [bib]: Silvio Amir; Ramón Astudillo; Wang Ling; Mario J. Silva; Isabel Trancoso
INESC-ID at SemEval-2016 Task 4-A: Reducing the Problem of Out-of-Embedding Words

S16-1037 [bib]: Cosmin Florean; Oana Bejenaru; Eduard Apostol; Octavian Ciobanu; Adrian Iftene; Diana Trandabat
SentimentalITsts at SemEval-2016 Task 4: building a Twitter sentiment analyzer in your backyard

S16-1038 [bib]: Calin-Cristian Ciubotariu; Marius-Valentin Hrisca; Mihail Gliga; Diana Darabana; Diana Trandabat; Adrian Iftene
Minions at SemEval-2016 Task 4: or how to build a sentiment analyzer using off-the-shelf resources?

S16-1039 [bib]: Yunchao He; Liang-Chih Yu; Chin-Sheng Yang; K. Robert Lai; Weiyi Liu
YZU-NLP Team at SemEval-2016 Task 4: Ordinal Sentiment Classification Using a Recurrent Convolutional Network

S16-1040 [bib]: Yunxiao Zhou; Zhihua Zhang; Man Lan
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

S16-1041 [bib]: Alexandra Balahur
OPAL at SemEval-2016 Task 4: the Challenge of Porting a Sentiment Analysis System to the "Real" World

S16-1042 [bib]: Stefan Falk; Andi Rexha; Roman Kern
Know-Center at SemEval-2016 Task 5: Using Word Vectors with Typed Dependencies for Opinion Target Expression Extraction

S16-1043 [bib]: Talaat Khalil; Samhaa R. El-Beltagy
NileTMRG at SemEval-2016 Task 5: Deep Convolutional Neural Networks for Aspect Category and Sentiment Extraction

S16-1044 [bib]: Caroline Brun; Julien Perez; Claude Roux
XRCE at SemEval-2016 Task 5: Feedbacked Ensemble Modeling on Syntactico-Semantic Knowledge for Aspect Based Sentiment Analysis

S16-1045 [bib]: Zhiqiang Toh; Jian Su
NLANGP at SemEval-2016 Task 5: Improving Aspect Based Sentiment Analysis using Neural Network Features

S16-1046 [bib]: Toshihiko Yanase; Kohsuke Yanai; Misa Sato; Toshinori Miyoshi; Yoshiki Niwa
bunji at SemEval-2016 Task 5: Neural and Syntactic Models of Entity-Attribute Relationship for Aspect-based Sentiment Analysis

S16-1047 [bib]: Maryna Chernyshevich
IHS-RD-Belarus at SemEval-2016 Task 5: Detecting Sentiment Polarity Using the Heatmap of Sentence

S16-1048 [bib]: Jakub Machacek
BUTknot at SemEval-2016 Task 5: Supervised Machine Learning with Term Substitution Approach in Aspect Category Detection

S16-1049 [bib]: Tamara Àlvarez-López; Jonathan Juncal-Martínez; Milagros Fernández-Gavilanes; Enrique Costa-Montenegro; Francisco Javier González-Castaño
GTI at SemEval-2016 Task 5: SVM and CRF for Aspect Detection and Unsupervised Aspect-Based Sentiment Analysis

S16-1050 [bib]: Dionysios Xenos; Panagiotis Theodorakakos; John Pavlopoulos; Prodromos Malakasiotis; Ion Androutsopoulos
AUEB-ABSA at SemEval-2016 Task 5: Ensembles of Classifiers and Embeddings for Aspect Based Sentiment Analysis

S16-1051 [bib]: Shubham Pateria; Prafulla Choubey
AKTSKI at SemEval-2016 Task 5: Aspect Based Sentiment Analysis for Consumer Reviews

S16-1052 [bib]: Vladimir Mayorov; Ivan Andrianov
MayAnd at SemEval-2016 Task 5: Syntactic and word2vec-based approach to aspect-based polarity detection in Russian

S16-1053 [bib]: Sebastian Ruder; Parsa Ghaffari; John G. Breslin
INSIGHT-1 at SemEval-2016 Task 5: Deep Learning for Multilingual Aspect-based Sentiment Analysis

S16-1054 [bib]: Fatih Samet Çetin; Ezgi Yıldırım; Can Özbey; Gülşen Eryiğit
TGB at SemEval-2016 Task 5: Multi-Lingual Constraint System for Aspect Based Sentiment Analysis

S16-1055 [bib]: Tomáš Hercig; Tomáš Brychcín; Lukáš Svoboda; Michal Konkol
UWB at SemEval-2016 Task 5: Aspect Based Sentiment Analysis

S16-1056 [bib]: Hussam Hamdan
SentiSys at SemEval-2016 Task 5: Opinion Target Extraction and Sentiment Polarity Detection

S16-1057 [bib]: Kim Schouten; Flavius Frasincar
COMMIT at SemEval-2016 Task 5: Sentiment Analysis with Rhetorical Structure Theory

S16-1058 [bib]: Mengxiao Jiang; Zhihua Zhang; Man Lan
ECNU at SemEval-2016 Task 5: Extracting Effective Features from Relevant Fragments in Sentence for Aspect-Based Sentiment Analysis in Reviews

S16-1059 [bib]: Aleš Tamchyna; Kateřina Veselovská
UFAL at SemEval-2016 Task 5: Recurrent Neural Networks for Sentence Classification

S16-1060 [bib]: Olga Vechtomova; Anni He
UWaterloo at SemEval-2016 Task 5: Minimally Supervised Approaches to Aspect-Based Sentiment Analysis

S16-1061 [bib]: Marcelo Dias; Karin Becker
INF-UFRGS-OPINION-MINING at SemEval-2016 Task 6: Automatic Generation of a Training Corpus for Unsupervised Identification of Stance in Tweets

S16-1062 [bib]: Wan Wei; Xiao Zhang; Xuqin Liu; Wei Chen; Tengjiao Wang
pkudblab at SemEval-2016 Task 6 : A Specific Convolutional Neural Network System for Effective Stance Detection

S16-1063 [bib]: Isabelle Augenstein; Andreas Vlachos; Kalina Bontcheva
USFD at SemEval-2016 Task 6: Any-Target Stance Detection on Twitter with Autoencoders

S16-1064 [bib]: Can Liu; Wen Li; Bradford Demarest; Yue Chen; Sara Couture; Daniel Dakota; Nikita Haduong; Noah Kaufman; Andrew Lamont; Manan Pancholi; Kenneth Steimel; Sandra Kübler
IUCL at SemEval-2016 Task 6: An Ensemble Model for Stance Detection in Twitter

S16-1065 [bib]: Yuki Igarashi; Hiroya Komatsu; Sosuke Kobayashi; Naoaki Okazaki; Kentaro Inui
Tohoku at SemEval-2016 Task 6: Feature-based Model versus Convolutional Neural Network for Stance Detection

S16-1066 [bib]: Peter Krejzl; Josef Steinberger
UWB at SemEval-2016 Task 6: Stance Detection

S16-1067 [bib]: Prashanth Vijayaraghavan; Ivan Sysoev; Soroush Vosoughi; Deb Roy
DeepStance at SemEval-2016 Task 6: Detecting Stance in Tweets Using Character and Word-Level CNNs

S16-1068 [bib]: Amita Misra; Brian Ecker; Theodore Handleman; Nicolas Hahn; Marilyn Walker
NLDS-UCSC at SemEval-2016 Task 6: A Semi-Supervised Approach to Detecting Stance in Tweets

S16-1069 [bib]: Michael Wojatzki; Torsten Zesch
ltl.uni-due at SemEval-2016 Task 6: Stance Detection in Social Media Using Stacked Classifiers

S16-1070 [bib]: Heba Elfardy; Mona Diab
CU-GWU Perspective at SemEval-2016 Task 6: Ideological Stance Detection in Informal Text

S16-1071 [bib]: Braja Gopal Patra; Dipankar Das; Sivaji Bandyopadhyay
JU_NLP at SemEval-2016 Task 6: Detecting Stance in Tweets using Support Vector Machines

S16-1072 [bib]: Henrik Bøhler; Petter Asla; Erwin Marsi; Rune Sætre
IDI$@$NTNU at SemEval-2016 Task 6: Detecting Stance in Tweets Using Shallow Features and GloVe Vectors for Word Representation

S16-1073 [bib]: Zhihua Zhang; Man Lan
ECNU at SemEval 2016 Task 6: Relevant or Not? Supportive or Not? A Two-step Learning System for Automatic Detecting Stance in Tweets

S16-1074 [bib]: Guido Zarrella; Amy Marsh
MITRE at SemEval-2016 Task 6: Transfer Learning for Stance Detection

S16-1075 [bib]: Martin Tutek; Ivan Sekulic; Paula Gombar; Ivan Paljak; Filip Culinovic; Filip Boltuzic; Mladen Karan; Domagoj Alagić; Jan Šnajder
TakeLab at SemEval-2016 Task 6: Stance Classification in Tweets Using a Genetic Algorithm Based Ensemble

S16-1076 [bib]: Amal Htait; Sebastien Fournier; Patrice Bellot
LSIS at SemEval-2016 Task 7: Using Web Search Engines for English and Arabic Unsupervised Sentiment Intensity Prediction

S16-1077 [bib]: Eshrag Refaee; Verena Rieser
iLab-Edinburgh at SemEval-2016 Task 7: A Hybrid Approach for Determining Sentiment Intensity of Arabic Twitter Phrases

S16-1078 [bib]: Ladislav Lenc; Pavel Král; Václav Rajtmajer
UWB at SemEval-2016 Task 7: Novel Method for Automatic Sentiment Intensity Determination

S16-1079 [bib]: Samhaa R. El-Beltagy
NileTMRG at SemEval-2016 Task 7: Deriving Prior Polarities for Arabic Sentiment Terms

S16-1080 [bib]: Feixiang Wang; Zhihua Zhang; Man Lan
ECNU at SemEval-2016 Task 7: An Enhanced Supervised Learning Method for Lexicon Sentiment Intensity Ranking

S16-1081 [bib]: Eneko Agirre; Carmen Banea; Daniel Cer; Mona Diab; Aitor Gonzalez-Agirre; Rada Mihalcea; German Rigau; Janyce Wiebe
SemEval-2016 Task 1: Semantic Textual Similarity, Monolingual and Cross-Lingual Evaluation

S16-1082 [bib]: Eneko Agirre; Aitor Gonzalez-Agirre; Inigo Lopez-Gazpio; Montse Maritxalar; German Rigau; Larraitz Uria
SemEval-2016 Task 2: Interpretable Semantic Textual Similarity

S16-1083 [bib]: Preslav Nakov; Lluís Màrquez; Alessandro Moschitti; Walid Magdy; Hamdy Mubarak; abed Alhakim Freihat; Jim Glass; Bilal Randeree
SemEval-2016 Task 3: Community Question Answering

S16-1084 [bib]: Nathan Schneider; Dirk Hovy; Anders Johannsen; Marine Carpuat
SemEval-2016 Task 10: Detecting Minimal Semantic Units and their Meanings (DiMSUM)

S16-1085 [bib]: Gustavo Paetzold; Lucia Specia
SemEval 2016 Task 11: Complex Word Identification

S16-1086 [bib]: Duygu Ataman; Jose G. C. De Souza; Marco Turchi; Matteo Negri
FBK HLT-MT at SemEval-2016 Task 1: Cross-lingual Semantic Similarity Measurement Using Quality Estimation Features and Compositional Bilingual Word Embeddings

S16-1087 [bib]: Sam Henry; Allison Sands
VRep at SemEval-2016 Task 1 and Task 2: A System for Interpretable Semantic Similarity

S16-1088 [bib]: Peng Li; Heng Huang
UTA DLNLP at SemEval-2016 Task 1: Semantic Textual Similarity: A Unified Framework for Semantic Processing and Evaluation

S16-1089 [bib]: Tomáš Brychcín; Lukáš Svoboda
UWB at SemEval-2016 Task 1: Semantic Textual Similarity using Lexical, Syntactic, and Semantic Information

S16-1090 [bib]: Matthias Liebeck; Philipp Pollack; Pashutan Modaresi; Stefan Conrad
HHU at SemEval-2016 Task 1: Multiple Approaches to Measuring Semantic Textual Similarity

S16-1091 [bib]: Barbara Rychalska; Katarzyna Pakulska; Krystyna Chodorowska; Wojciech Walczak; Piotr Andruszkiewicz
Samsung Poland NLP Team at SemEval-2016 Task 1: Necessity for diversity; combining recursive autoencoders, WordNet and ensemble methods to measure semantic similarity.

S16-1092 [bib]: Ahmet Aker; Frederic Blain; Andres Duque; Marina Fomicheva; Jurica Seva; Kashif Shah; Daniel Beck
USFD at SemEval-2016 Task 1: Putting different State-of-the-Arts into a Box

S16-1093 [bib]: Piotr Przybyła; Nhung T. H. Nguyen; Matthew Shardlow; Georgios Kontonatsios; Sophia Ananiadou
NaCTeM at SemEval-2016 Task 1: Inferring sentence-level semantic similarity from an ensemble of complementary lexical and sentence-level features

S16-1094 [bib]: Junfeng Tian; Man Lan
ECNU at SemEval-2016 Task 1: Leveraging Word Embedding From Macro and Micro Views to Boost Performance for Semantic Textual Similarity

S16-1095 [bib]: Liling Tan; Carolina Scarton; Lucia Specia; Josef van Genabith
SAARSHEFF at SemEval-2016 Task 1: Semantic Textual Similarity with Machine Translation Evaluation Metrics and (eXtreme) Boosted Tree Ensembles

S16-1096 [bib]: Hannah Bechara; Rohit Gupta; Liling Tan; Constantin Orasan; Ruslan Mitkov; Josef van Genabith
WOLVESAAR at SemEval-2016 Task 1: Replicating the Success of Monolingual Word Alignment and Neural Embeddings for Semantic Textual Similarity

S16-1097 [bib]: Rajendra Banjade; Nabin Maharjan; Dipesh Gautam; Vasile Rus
DTSim at SemEval-2016 Task 1: Semantic Similarity Model Including Multi-Level Alignment and Vector-Based Compositional Semantics

S16-1098 [bib]: Cheng Fu; Bo An; Xianpei Han; Le Sun
ISCAS_NLP at SemEval-2016 Task 1: Sentence Similarity Based on Support Vector Regression using Multiple Features

S16-1099 [bib]: Md Arafat Sultan; Steven Bethard; Tamara Sumner
DLS$@$CU at SemEval-2016 Task 1: Supervised Models of Sentence Similarity

S16-1100 [bib]: Chris Hokamp; Piyush Arora
DCU-SEManiacs at SemEval-2016 Task 1: Synthetic Paragram Embeddings for Semantic Textual Similarity

S16-1101 [bib]: Hanan Aldarmaki; Mona Diab
GWU NLP at SemEval-2016 Shared Task 1: Matrix Factorization for Crosslingual STS

S16-1102 [bib]: Chi-kiu Lo; Cyril Goutte; Michel Simard
CNRC at SemEval-2016 Task 1: Experiments in Crosslingual Semantic Textual Similarity

S16-1103 [bib]: Naveed Afzal; Yanshan Wang; Hongfang Liu
MayoNLP at SemEval-2016 Task 1: Semantic Textual Similarity based on Lexical Semantic Net and Deep Learning Semantic Model

S16-1104 [bib]: Harish Tayyar Madabushi; Mark Buhagiar; Mark Lee
UoB-UK at SemEval-2016 Task 1: A Flexible and Extendable System for Semantic Text Similarity using Types, Surprise and Phrase Linking

S16-1105 [bib]: Hao Wu; Heyan Huang; Wenpeng Lu
BIT at SemEval-2016 Task 1: Sentence Similarity Based on Alignments and Vector with the Weight of Information Content

S16-1106 [bib]: Hideo Itoh
RICOH at SemEval-2016 Task 1: IR-based Semantic Textual Similarity Estimation

S16-1107 [bib]: Maryna Beliuha; Maryna Chernyshevich
IHS-RD-Belarus at SemEval-2016 Task 1: Multistage Approach for Measuring Semantic Similarity

S16-1108 [bib]: Sandip Sarkar; Dipankar Das; Partha Pakray; Alexander Gelbukh
JUNITMZ at SemEval-2016 Task 1: Identifying Semantic Similarity Using Levenshtein Ratio

S16-1109 [bib]: Barathi Ganesh HB; Anand Kumar M; Soman KP
Amrita_CEN at SemEval-2016 Task 1: Semantic Relation from Word Embeddings in Higher Dimension

S16-1110 [bib]: John Philip McCrae; Kartik Asooja; Nitish Aggarwal; Paul Buitelaar
NUIG-UNLP at SemEval-2016 Task 1: Soft Alignment and Deep Learning for Semantic Textual Similarity

S16-1111 [bib]: kolawole adebayo; Luigi Di Caro; Guido Boella
NORMAS at SemEval-2016 Task 1: SEMSIM: A Multi-Feature Approach to Semantic Text Similarity

S16-1112 [bib]: Oscar William Lightgow Serrano; Ivan Vladimir Meza Ruiz; Albert Manuel Orozco Camacho; Jorge Garcia Flores; Davide Buscaldi
LIPN-IIMAS at SemEval-2016 Task 1: Random Forest Regression Experiments on Align-and-Differentiate and Word Embeddings penalizing strategies

S16-1113 [bib]: Milton King; Waseem Gharbieh; SoHyun Park; Paul Cook
UNBNLP at SemEval-2016 Task 1: Semantic Textual Similarity: A Unified Framework for Semantic Processing and Evaluation

S16-1114 [bib]: Asli Eyecioglu; Bill Keller
ASOBEK at SemEval-2016 Task 1: Sentence Representation with Character N-gram Embeddings for Semantic Textual Similarity

S16-1115 [bib]: Peter Potash; William Boag; Alexey Romanov; Vasili Ramanishka; Anna Rumshisky
SimiHawk at SemEval-2016 Task 1: A Deep Ensemble System for Semantic Textual Similarity

S16-1116 [bib]: Sergio Jimenez
SERGIOJIMENEZ at SemEval-2016 Task 1: Effectively Combining Paraphrase Database, String Matching, WordNet, and Word Embedding for Semantic Textual Similarity

S16-1117 [revisions: v2] [bib]: Ergun Bicici
RTM at SemEval-2016 Task 1: Predicting Semantic Similarity with Referential Translation Machines and Related Statistics

S16-1118 [bib]: Jie Mei; Aminul Islam; Evangelos Milios
DalGTM at SemEval-2016 Task 1: Importance-Aware Compositional Approach to Short Text Similarity

S16-1119 [bib]: Inigo Lopez-Gazpio; Eneko Agirre; Montse Maritxalar
iUBC at SemEval-2016 Task 2: RNNs and LSTMs for interpretable STS

S16-1120 [bib]: ping tan; Karin Verspoor; Timothy Miller
Rev at SemEval-2016 Task 2: Aligning Chunks by Lexical, Part of Speech and Semantic Equivalence

S16-1121 [bib]: Simone Magnolini; Anna Feltracco; Bernardo Magnini
FBK-HLT-NLP at SemEval-2016 Task 2: A Multitask, Deep Learning Approach for Interpretable Semantic Textual Similarity

S16-1122 [bib]: Lavanya Tekumalla; Sharmistha Jat
IISCNLP at SemEval-2016 Task 2: Interpretable STS with ILP based Multiple Chunk Aligner

S16-1123 [bib]: Rodolfo Delmonte
VENSESEVAL at Semeval-2016 Task 2 iSTS - with a full-fledged rule-based approach

S16-1124 [bib]: Miloslav Konopik; Ondrej Prazak; David Steinberger; Tomáš Brychcín
UWB at SemEval-2016 Task 2: Interpretable Semantic Textual Similarity with Distributional Semantics for Chunks

S16-1125 [bib]: Rajendra Banjade; Nabin Maharjan; Nobal Bikram Niraula; Vasile Rus
DTSim at SemEval-2016 Task 2: Interpreting Similarity of Texts Based on Automated Chunking, Chunk Alignment and Semantic Relation Prediction

S16-1126 [bib]: Marc Franco-Salvador; Sudipta Kar; Thamar Solorio; Paolo Rosso
UH-PRHLT at SemEval-2016 Task 3: Combining Lexical and Semantic-based Features for Community Question Answering

S16-1127 [bib]: Ahmed Magooda; Amr Gomaa; Ashraf Mahgoub; Hany Ahmed; Mohsen Rashwan; Hazem Raafat; Eslam Kamal; Ahmad Al Sallab
RDI_Team at SemEval-2016 Task 3: RDI Unsupervised Framework for Text Ranking

S16-1128 [bib]: Mitra Mohtarami; Yonatan Belinkov; Wei-Ning Hsu; Yu Zhang; Tao Lei; Kfir Bar; Scott Cyphers; Jim Glass
SLS at SemEval-2016 Task 3: Neural-based Approaches for Ranking in Community Question Answering

S16-1129 [bib]: Tsvetomila Mihaylova; Pepa Gencheva; Martin Boyanov; Ivana Yovcheva; Todor Mihaylov; Momchil Hardalov; Yasen Kiprov; Daniel Balchev; Ivan Koychev; Preslav Nakov; Ivelina Nikolova; Galia Angelova
SUper Team at SemEval-2016 Task 3: Building a Feature-Rich System for Community Question Answering

S16-1130 [bib]: Daniel Balchev; Yasen Kiprov; Ivan Koychev; Preslav Nakov
PMI-cool at SemEval-2016 Task 3: Experiments with PMI and Goodness Polarity Lexicons for Community Question Answering

S16-1131 [bib]: Timothy Baldwin; Huizhi Liang; Bahar Salehi; Doris Hoogeveen; Yitong Li; Long Duong
UniMelb at SemEval-2016 Task 3: Identifying Similar Questions by combining a CNN with String Similarity Measures

S16-1132 [bib]: Yunfang Wu; Minghua Zhang
ICL00 at SemEval-2016 Task 3: Translation-Based Method for CQA System

S16-1133 [bib]: Hujie Wang; Pascal Poupart
Overfitting at SemEval-2016 Task 3: Detecting Semantically Similar Questions in Community Question Answering Forums with Word Embeddings

S16-1134 [bib]: Rana Malhas; Marwan Torki; Tamer Elsayed
QU-IR at SemEval 2016 Task 3: Learning to Rank on Arabic Community Question Answering Forums with Word Embedding

S16-1135 [bib]: Guoshun Wu; Man Lan
ECNU at SemEval-2016 Task 3: Exploring Traditional Method and Deep Learning Method for Question Retrieval and Answer Ranking in Community Question Answering

S16-1136 [bib]: Todor Mihaylov; Preslav Nakov
SemanticZ at SemEval-2016 Task 3: Ranking Relevant Answers in Community Question Answering Using Semantic Similarity Based on Fine-tuned Word Embeddings

S16-1137 [bib]: Francisco Guzmán; Preslav Nakov; Lluís Màrquez
MTE-NN at SemEval-2016 Task 3: Can Machine Translation Evaluation Help Community Question Answering?

S16-1138 [bib]: Alberto Barrón-Cedeño; Giovanni Da San Martino; Shafiq Joty; Alessandro Moschitti; Fahad Al-Obaidli; Salvatore Romeo; Kateryna Tymoshenko; Antonio Uva
ConvKN at SemEval-2016 Task 3: Answer and Question Selection for Question Answering on Arabic and English Fora

S16-1139 [bib]: Chang e Jia
ITNLP-AiKF at SemEval-2016 Task 3 a quesiton answering system using community QA repository

S16-1140 [bib]: Silvio Cordeiro; Carlos Ramisch; Aline Villavicencio
UFRGS&LIF at SemEval-2016 Task 10: Rule-Based MWE Identification and Predominant-Supersense Tagging

S16-1141 [bib]: Xin Tang; Fei Li; Donghong Ji
WHUNlp at SemEval-2016 Task DiMSUM: A Pilot Study in Detecting Minimal Semantic Units and their Meanings using Supervised Models

S16-1142 [bib]: Jari Björne; Tapio Salakoski
UTU at SemEval-2016 Task 10: Binary Classification for Expression Detection (BCED)

S16-1143 [bib]: Mohammad Javad Hosseini; Noah A. Smith; Su-In Lee
UW-CSE at SemEval-2016 Task 10: Detecting Multiword Expressions and Supersenses using Double-Chained Conditional Random Fields

S16-1144 [bib]: Angelika Kirilin; Felix Krauss; Yannick Versley
ICL-HD at SemEval-2016 Task 10: Improving the Detection of Minimal Semantic Units and their Meanings with an Ontology and Word Embeddings

S16-1145 [bib]: Andreas Scherbakov; Ekaterina Vylomova; Fei Liu; Timothy Baldwin
VectorWeavers at SemEval-2016 Task 10: From Incremental Meaning to Semantic Unit (phrase by phrase)

S16-1146 [bib]: Krzysztof Wróbel
PLUJAGH at SemEval-2016 Task 11: Simple System for Complex Word Identification

S16-1147 [bib]: José Manuel Martínez Martínez; Liling Tan
USAAR at SemEval-2016 Task 11: Complex Word Identification with Sense Entropy and Sentence Perplexity

S16-1148 [bib]: Gillin Nat
Sensible at SemEval-2016 Task 11: Neural Nonsense Mangled in Ensemble Mess

S16-1149 [bib]: Gustavo Paetzold; Lucia Specia
SV000gg at SemEval-2016 Task 11: Heavy Gauge Complex Word Identification with System Voting

S16-1150 [bib]: Julian Brooke; Alexandra Uitdenbogerd; Timothy Baldwin
Melbourne at SemEval 2016 Task 11: Classifying Type-level Word Complexity using Random Forests with Corpus and Word List Features

S16-1151 [bib]: Elnaz Davoodi; Leila Kosseim
CLaC at SemEval-2016 Task 11: Exploring linguistic and psycho-linguistic Features for Complex Word Identification

S16-1152 [bib]: Niloy Mukherjee; Braja Gopal Patra; Dipankar Das; Sivaji Bandyopadhyay
JU_NLP at SemEval-2016 Task 11: Identifying Complex Words in a Sentence

S16-1153 [bib]: Shervin Malmasi; Marcos Zampieri
MAZA at SemEval-2016 Task 11: Detecting Lexical Complexity Using a Decision Stump Meta-Classifier

S16-1154 [bib]: Shervin Malmasi; Mark Dras; Marcos Zampieri
LTG at SemEval-2016 Task 11: Complex Word Identification with Classifier Ensembles

S16-1155 [bib]: Marcos Zampieri; Liling Tan; Josef van Genabith
MacSaar at SemEval-2016 Task 11: Zipfian and Character Features for ComplexWord Identification

S16-1156 [bib]: Prafulla Choubey; Shubham Pateria
Garuda & Bhasha at SemEval-2016 Task 11: Complex Word Identification Using Aggregated Learning Models

S16-1157 [bib]: Francesco Ronzano; Ahmed Abura'ed; Luis Espinosa Anke; Horacio Saggion
TALN at SemEval-2016 Task 11: Modelling Complex Words by Contextual, Lexical and Semantic Features

S16-1158 [bib]: Ashish Palakurthi; Radhika Mamidi
IIIT at SemEval-2016 Task 11: Complex Word Identification using Nearest Centroid Classification

S16-1159 [bib]: sanjay sp; Anand Kumar; Soman K P
AmritaCEN at SemEval-2016 Task 11: Complex Word Identification using Word Embedding

S16-1160 [bib]: Joachim Bingel; Natalie Schluter; Héctor Martínez Alonso
CoastalCPH at SemEval-2016 Task 11: The importance of designing your Neural Networks right

S16-1161 [bib]: Maury Quijada; Julie Medero
HMC at SemEval-2016 Task 11: Identifying Complex Words Using Depth-limited Decision Trees

S16-1162 [bib]: Michal Konkol
UWB at SemEval-2016 Task 11: Exploring Features for Complex Word Identification

S16-1163 [bib]: Onur Kuru
AI-KU at SemEval-2016 Task 11: Word Embeddings and Substring Features for Complex Word Identification

S16-1164 [bib]: David Kauchak
Pomona at SemEval-2016 Task 11: Predicting Word Complexity Based on Corpus Frequency

S16-1165 [revisions: v2] [bib]: Steven Bethard; Guergana Savova; Wei-Te Chen; Leon Derczynski; James Pustejovsky; Marc Verhagen
SemEval-2016 Task 12: Clinical TempEval

S16-1166 [bib]: Jonathan May
SemEval-2016 Task 8: Meaning Representation Parsing

S16-1167 [bib]: Wanxiang Che; Yanqiu Shao; Ting Liu; Yu Ding
SemEval-2016 Task 9: Chinese Semantic Dependency Parsing

S16-1168 [bib]: Georgeta Bordea; Els Lefever; Paul Buitelaar
SemEval-2016 Task 13: Taxonomy Extraction Evaluation (TExEval-2)

S16-1169 [bib]: David Jurgens; Mohammad Taher Pilehvar
SemEval-2016 Task 14: Semantic Taxonomy Enrichment

S16-1170 [bib]: Hua He; John Wieting; Kevin Gimpel; Jinfeng Rao; Jimmy Lin
UMD-TTIC-UW at SemEval-2016 Task 1: Attention-Based Multi-Perspective Convolutional Neural Networks for Textual Similarity Measurement

S16-1171 [bib]: Mishal Kazmi; Peter Schüller
Inspire at SemEval-2016 Task 2: Interpretable Semantic Textual Similarity Alignment based on Answer Set Programming

S16-1172 [bib]: Simone Filice; Danilo Croce; Alessandro Moschitti; Roberto Basili
KeLP at SemEval-2016 Task 3: Learning Semantic Relations between Questions and Answers

S16-1173 [bib]: Jan Deriu; Maurice Gonzenbach; Fatih Uzdilli; Aurelien Lucchi; Valeria De Luca; Martin Jaggi
SwissCheese at SemEval-2016 Task 4: Sentiment Classification Using an Ensemble of Convolutional Neural Networks with Distant Supervision

S16-1174 [bib]: Ayush Kumar; Sarah Kohail; Amit Kumar; Asif Ekbal; Chris Biemann
IIT-TUDA at SemEval-2016 Task 5: Beyond Sentiment Lexicon: Combining Domain Dependency and Distributional Semantics Features for Aspect Based Sentiment Analysis

S16-1175 [bib]: Julien Tourille; Olivier Ferret; Aurélie Névéol; Xavier Tannier
LIMSI-COT at SemEval-2016 Task 12: Temporal relation identification using a pipeline of classifiers

S16-1176 [bib]: Guntis Barzdins; Didzis Gosko
RIGA at SemEval-2016 Task 8: Impact of Smatch Extensions and Character-Level Neural Translation on AMR Parsing Accuracy

S16-1177 [bib]: Alastair Butler
DynamicPower at SemEval-2016 Task 8: Processing syntactic parse trees with a Dynamic Semantics core

S16-1178 [bib]: Yevgeniy Puzikov; Daisuke Kawahara; Sadao Kurohashi
M2L at SemEval-2016 Task 8: AMR Parsing with Neural Networks

S16-1179 [bib]: Lauritz Brandt; David Grimm; Mengfei Zhou; Yannick Versley
ICL-HD at SemEval-2016 Task 8: Meaning Representation Parsing - Augmenting AMR Parsing with a Preposition Semantic Role Labeling Neural Network

S16-1180 [bib]: James Goodman; Andreas Vlachos; Jason Naradowsky
UCL+Sheffield at SemEval-2016 Task 8: Imitation learning for AMR parsing with an alpha-bound

S16-1181 [bib]: Chuan Wang; Sameer Pradhan; Xiaoman Pan; Heng Ji; Nianwen Xue
CAMR at SemEval-2016 Task 8: An Extended Transition-based AMR Parser

S16-1182 [bib]: Johannes Bjerva; Johan Bos; Hessel Haagsma
The Meaning Factory at SemEval-2016 Task 8: Producing AMRs with Boxer

S16-1183 [bib]: Xiaochang Peng; Daniel Gildea
UofR at SemEval-2016 Task 8: Learning Synchronous Hyperedge Replacement Grammar for AMR Parsing

S16-1184 [bib]: Sudha Rao; Yogarshi Vyas; Hal Daumé III; Philip Resnik
CLIP$@$UMD at SemEval-2016 Task 8: Parser for Abstract Meaning Representation using Learning to Search

S16-1185 [bib]: William Foland; James H. Martin
CU-NLP at SemEval-2016 Task 8: AMR Parsing using LSTM-based Recurrent Neural Networks

S16-1186 [bib]: Jeffrey Flanigan; Chris Dyer; Noah A. Smith; Jaime Carbonell
CMU at SemEval-2016 Task 8: Graph-based AMR Parsing with Infinite Ramp Loss

S16-1187 [bib]: Artsiom Artsymenia; Palina Dounar; Maria Yermakovich
IHS-RD-Belarus at SemEval-2016 Task 9: Transition-based Chinese Semantic Dependency Parsing with Online Reordering and Bootstrapping.

S16-1188 [bib]: Lifeng Jin; Manjuan Duan; William Schuler
OCLSP at SemEval-2016 Task 9: Multilayered LSTM as a Neural Semantic Dependency Parser

S16-1189 [bib]: Manjuan Duan; Lifeng Jin; William Schuler
OSU_CHGCG at SemEval-2016 Task 9 : Chinese Semantic Dependency Parsing with Generalized Categorial Grammar

S16-1190 [bib]: Cyril Grouin; Véronique MORICEAU
LIMSI at SemEval-2016 Task 12: machine-learning and temporal information to identify clinical events and time expressions

S16-1191 [bib]: Sarath P R; Manikandan R; Yoshiki Niwa
Hitachi at SemEval-2016 Task 12: A Hybrid Approach for Temporal Information Extraction from Clinical Notes

S16-1192 [bib]: Veera Raghavendra Chikka
CDE-IIITH at SemEval-2016 Task 12: Extraction of Temporal Information from Clinical documents using Machine Learning techniques

S16-1193 [bib]: Tommaso Caselli; Roser Morante
VUACLTL at SemEval 2016 Task 12: A CRF Pipeline to Clinical TempEval

S16-1194 [bib]: Arman Cohan; Kevin Meurer; Nazli Goharian
GUIR at SemEval-2016 task 12: Temporal Information Processing for Clinical Narratives

S16-1195 [bib]: Abdulrahman AAl Abdulsalam; Sumithra Velupillai; Stephane Meystre
UtahBMI at SemEval-2016 Task 12: Extracting Temporal Information from Clinical Text

S16-1196 [bib]: Marcia Barros; André Lamúrias; Gonçalo Figueiró; Marta Antunes; Joana Teixeira; Alexandre Pinheiro; Francisco M. Couto
ULISBOA at SemEval-2016 Task 12: Extraction of temporal expressions, clinical events and relations using IBEnt

S16-1197 [bib]: Peng Li; Heng Huang
UTA DLNLP at SemEval-2016 Task 12: Deep Learning Based Natural Language Processing System for Clinical Information Identification from Clinical Notes and Pathology Reports

S16-1198 [bib]: Jason Fries
Brundlefly at SemEval-2016 Task 12: Recurrent Neural Networks vs. Joint Inference for Clinical Temporal Information Extraction

S16-1199 [bib]: Artuur Leeuwenberg; Marie-Francine Moens
KULeuven-LIIR at SemEval 2016 Task 12: Detecting Narrative Containment in Clinical Records

S16-1200 [bib]: Charlotte Hansart; Damien De Meyere; Patrick Watrin; André Bittar; Cédrick Fairon
CENTAL at SemEval-2016 Task 12: a linguistically fed CRF model for medical and temporal information extraction

S16-1201 [bib]: Hee-Jin Lee; Hua Xu; Jingqi Wang; Yaoyun Zhang; Sungrim Moon; Jun Xu; Yonghui Wu
UTHealth at SemEval-2016 Task 12: an End-to-End System for Temporal Information Extraction from Clinical Notes

S16-1202 [bib]: Joel Pocostales
NUIG-UNLP at SemEval-2016 Task 13: A Simple Word Embedding-based Approach for Taxonomy Extraction

S16-1203 [bib]: Liling Tan; Francis Bond; Josef van Genabith
USAAR at SemEval-2016 Task 13: Hyponym Endocentricity

S16-1204 [bib]: Promita Maitra; Dipankar Das
JUNLP at SemEval-2016 Task 13: A Language Independent Approach for Hypernym Identification

S16-1205 [bib]: Guillaume Cleuziou; Jose G. Moreno
QASSIT at SemEval-2016 Task 13: On the integration of Semantic Vectors in Pretopological Spaces for Lexical Taxonomy Acquisition

S16-1206 [bib]: Alexander Panchenko; Stefano Faralli; Eugen Ruppert; Steffen Remus; Hubert Naets; Cedrick Fairon; Simone Paolo Ponzetto; Chris Biemann
TAXI at SemEval-2016 Task 13: a Taxonomy Induction Method based on Lexico-Syntactic Patterns, Substrings and Focused Crawling

S16-1207 [bib]: Ted Pedersen
Duluth at SemEval 2016 Task 14: Extending Gloss Overlaps to Enrich Semantic Taxonomies

S16-1208 [bib]: Luis Espinosa Anke; Francesco Ronzano; Horacio Saggion
TALN at SemEval-2016 Task 14: Semantic Taxonomy Enrichment Via Sense-Based Embeddings

S16-1209 [bib]: Michael Schlichtkrull; Héctor Martínez Alonso
MSejrKu at SemEval-2016 Task 14: Taxonomy Enrichment by Evidence Ranking

S16-1210 [bib]: Hristo Tanev; Agata Rotondi
Deftor at SemEval-2016 Task 14: Taxonomy enrichment using definition vectors

S16-1211 [bib]: Jon Rusert; Ted Pedersen
UMNDuluth at SemEval-2016 Task 14: WordNet's Missing Lemmas

S16-1212 [bib]: Bridget McInnes
VCU at Semeval-2016 Task 14: Evaluating definitional-based similarity measure for semantic taxonomy enrichment

Proceedings of the Fifth Joint Conference on Lexical and Computational Semantics

S16-2 [bib]: Entire volume

S16-2000: Front Matter

S16-2001 [bib]: Tal Linzen; Emmanuel Dupoux; Benjamin Spector
Quantificational features in distributional word representations

S16-2002 [bib]: Ingrid Falk; Fabienne Martin
Automatic Identification of Aspectual Classes across Verbal Readings

S16-2003 [bib]: Saif Mohammad; Ekaterina Shutova; Peter Turney
Metaphor as a Medium for Emotion: An Empirical Study

S16-2004 [bib]: Gene Kim; Lenhart Schubert
High-Fidelity Lexical Axiom Construction from Verb Glosses

S16-2005 [bib]: Jennifer Sikos; Yannick Versley; Anette Frank
Implicit Semantic Roles in a Multilingual Setting

S16-2006 [bib]: Meriem Beloucif; Dekai Wu
Driving inversion transduction grammar induction with semantic evaluation

S16-2007 [bib]: Lasha Abzianidze
Natural Solution to FraCaS Entailment Problems

S16-2008 [bib]: Manfred Klenner; Simon Clematide
How Factuality Determines Sentiment Inferences

S16-2009 [bib]: Linfeng Song; Zhiguo Wang; Haitao Mi; Daniel Gildea
Sense Embedding Learning for Word Sense Induction

S16-2010 [bib]: Maximilian Köper; Sabine Schulte im Walde; Max Kisselew; Sebastian Padó
Improving Zero-Shot-Learning for German Particle Verbs by using Training-Space Restrictions and Local Scaling

S16-2011 [revisions: v2] [bib]: Alicia Krebs; Denis Paperno
When Hyperparameters Help: Beneficial Parameter Combinations in Distributional Semantic Models

S16-2012 [bib]: Daniel Peterson; Jordan Boyd-Graber; Martha Palmer; Daisuke Kawahara
Leveraging VerbNet to build Corpus-Specific Verb Clusters

S16-2013 [bib]: Vered Shwartz; Ido Dagan
Adding Context to Semantic Data-Driven Paraphrasing

S16-2014 [bib]: Ellie Pavlick; Chris Callison-Burch
So-Called Non-Subsective Adjectives

S16-2015 [bib]: Elise van der Pol; Sharon Gieske; Raquel Fernandez
Linguistic Style Accommodation in Disagreements

S16-2016 [bib]: Goran Glavaš; Federico Nanni; Simone Paolo Ponzetto
Unsupervised Text Segmentation Using Semantic Relatedness Graphs

S16-2017 [bib]: Leen Sevens; Gilles Jacobs; Vincent Vandeghinste; Ineke Schuurman; Frank Van Eynde
Improving Text-to-Pictograph Translation Through Word Sense Disambiguation

S16-2018 [bib]: Azad Abad; Alessandro Moschitti
Taking the best from the Crowd:Learning Question Passage Classification from Noisy Data

S16-2019 [bib]: Chunyang Xiao; Guillaume Bouchard; Marc Dymetman; Claire Gardent
Orthogonality regularizer for question answering

S16-2020 [bib]: Sabine Schulte im Walde; Anna Hätty; Stefan Bott
The Role of Modifier and Head Properties in Predicting the Compositionality of English and German Noun-Noun Compounds: A Vector-Space Perspective

S16-2021 [bib]: Parinaz Sobhani; Saif Mohammad; Svetlana Kiritchenko
Detecting Stance in Tweets And Analyzing its Interaction with Sentiment

S16-2022 [bib]: Sapna Negi; Kartik Asooja; Shubham Mehrotra; Paul Buitelaar
A Study of Suggestions in Opinionated Texts and their Automatic Detection

S16-2023 [bib]: Aurėlie Herbelot; Behrang QasemiZadeh
You and me... in a vector space: modelling individual speakers with distributional semantics

S16-2024 [bib]: Behrang QasemiZadeh; Laura Kallmeyer
Random Positive-Only Projections: PPMI-Enabled Incremental Semantic Space Construction

S16-2025 [bib]: Juliano Efson Sales; Andre Freitas; Brian Davis; Siegfried Handschuh
A Compositional-Distributional Semantic Model for Searching Complex Entity Categories

S16-2026 [bib]: Ramon Ziai; Kordula De Kuthy; Detmar Meurers
Approximating Givenness in Content Assessment through Distributional Semantics

S16-2027 [bib]: Laura Perez-Beltrachini; Claire Gardent
Learning Embeddings to lexicalise RDF Properties