NLANGP: Supervised Machine Learning System for Aspect Category Classification and Opinion Target Extraction

NLANGP: Supervised Machine Learning System for Aspect Category Classification and Opinion Target Extraction
Title:
NLANGP: Supervised Machine Learning System for Aspect Category Classification and Opinion Target Extraction
Other Titles:
Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)
DOI:
Publication Date:
04 June 2015
Citation:
Abstract:
This paper describes our system used in the Aspect Based Sentiment Analysis Task 12 of SemEval-2015. Our system is based on two supervised machine learning algorithms: sigmoidal feedforward network to train binary classifiers for aspect category classification (Slot 1), and Conditional Random Fields to train classifiers for opinion target extraction (Slot 2). We extract a variety of lexicon and syntactic features, as well as cluster features induced from unlabeled data. Our system achieves state-of-the-art performances, ranking 1st for three of the evaluations (Slot 1 for both restaurant and laptop domains, and Slot 1 & 2) and 2nd for Slot 2 evaluation.
License type:
http://creativecommons.org/licenses/by/4.0/
Funding Info:
Description:
ISSN:
978-1-941643-40-2
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