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*SEMEVAL - 2016
Total Papers:- 240
Total Papers accross all years:- 1898
Total Citations :- 1236
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The Meaning Factory at SemEval-2016 Task 8: Producing AMRs with Boxer
Johannes Bjerva |
Johan Bos |
Hessel Haagsma |
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 |
ULISBOA at SemEval-2016 Task 12: Extraction of temporal expressions, clinical events and relations using IBEnt
Marcia Barros |
Andre Lamurias |
Gonçalo Figueiro |
Marta Antunes |
Joana Teixeira |
Alexandre Pinheiro |
Francisco M. Couto |
How Factuality Determines Sentiment Inferences
Manfred Klenner |
Simon Clematide |
evision PDF of 'When Hyperparameters Help: Beneficial Parameter Combinations in Distributional Semantic Models
Alicia Krebs |
Denis Paperno |
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 |
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 |
NRU-HSE at SemEval-2016 Task 4: Comparative Analysis of Two Iterative Methods Using Quantification Library
Nikolay Karpov |
Alexander Porshnev |
Kirill Rudakov |
INSIGHT-1 at SemEval-2016 Task 4: Convolutional Neural Networks for Sentiment Classification and Quantification
Sebastian Ruder |
Parsa Ghaffari |
John G. Breslin |
DiegoLab16 at SemEval-2016 Task 4: Sentiment Analysis in Twitter using Centroids, Clusters, and Sentiment Lexicons
Abeed Sarker |
Graciela Gonzalez |
PotTS at SemEval-2016 Task 4: Sentiment Analysis of Twitter Using Character-level Convolutional Neural Networks.
Uladzimir Sidarenka |
Minions at SemEval-2016 Task 4: or how to build a sentiment analyzer using off-the-shelf resources?
Călin-Cristian Ciubotariu |
Marius-Valentin Hrişca |
Mihail Gliga |
Diana Darabană |
Diana Trandabăţ |
Adrian Iftene |
Know-Center at SemEval-2016 Task 5: Using Word Vectors with Typed Dependencies for Opinion Target Expression Extraction
Stefan Falk |
Andi Rexha |
Roman Kern |
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 |
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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