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Theimplementationofthepaperisavailableathttps://www.cs.uic.edu/~hxu/
ArchitectureofDE-CNN:redvectorsarepaddingvectors
Double Embeddings and CNN-based Sequence Labeling for Aspect Extraction HuXu1,BingLiu1,LeiShu1,PhilipS.Yu1,21DepartmentofComputerScience,UniversityofIllinoisatChicago2InstituteforDataScience,TsinghuaUniversity
{hxu48, liub, lshu3, psyu}@uic.edu
Ther-thCNNfilterforthei-thwordinlayerl
AspectExtractionisanimportanttaskinfine-grainedsentimentanalysis.Weproposeasimpleandfastapproachwithoutusinganysophisticatedfeaturesandmodels.Thecontributionsarein2folds:Doubleembedding:weusetwotypesofpre-trainedembeddingsforaspectextraction:generalpurposeembeddingsanddomainspecificembeddings.CNN:weuseCNNforsequencelabeling,whichisparallelandfasterthanserialLSTM.WeadaptCNN(e.g.,dropmax-poolinglayer)togetbetterresults.