AUEB-ABSA at SemEval-2016 Task 5: Ensembles of Classifiers and Embeddings for Aspect Based Sentiment Analysis
Published 1 January 2016Open access
Dionysios P. Xenos, Panagiotis Theodorakakos, John Pavlopoulos, Prodromos Malakasiotis, Ion Androutsopoulos
Citations36
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TL;DR
Their submissions to the Aspect Based Sentiment Analysis task of SemEval-2016 used multiple ensembles, based on Support Vector Machine classifiers, and were ranked in the top 6 positions in all the tasks the authors participated.
Abstract
This paper describes our submissions to the Aspect Based Sentiment Analysis task of SemEval-2016.For Aspect Category Detection (Subtask1/Slot1), we used multiple ensembles, based on Support Vector Machine classifiers.For Opinion Target Expression extraction (Subtask1/Slot2), we used a sequence labeling approach with Conditional Random Fields.For Polarity Detection (Sub-task1/Slot3), we used an ensemble of two supervised classifiers, one based on hand crafted features and one based on word embeddings.Our systems were ranked in the top 6 positions in all the tasks we participated.The source code of our systems is publicly available.
Keywords
Computer Science
TechnometricsStatistical Learning Theory
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