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The implementation of the paper is available at https://www.cs.uic.edu/~hxu/ Architecture of DE-CNN: red vectors are padding vectors Double Embeddings and CNN-based Sequence Labeling for Aspect Extraction Hu Xu 1 , Bing Liu 1 , Lei Shu 1 , Philip S. Yu 1,2 1 Department of Computer Science, University of Illinois at Chicago 2 Institute for Data Science, Tsinghua University {hxu48, liub, lshu3, psyu}@uic.edu The r-th CNN filter for the i-th word in layer l Aspect Extraction is an important task in fine-grained sentiment analysis. We propose a simple and fast approach without using any sophisticated features and models. The contributions are in 2 folds: Double embedding: we use two types of pre-trained embeddings for aspect extraction: general purpose embeddings and domain specific embeddings. CNN: we use CNN for sequence labeling, which is parallel and faster than serial LSTM. We adapt CNN (e.g., drop max- pooling layer) to get better results.

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Page 1: Double Embeddings and CNN-based Sequence Labelinghxu/ACL_poster.pdf · Double Embeddings and CNN-based Sequence Labeling for Aspect Extraction Hu Xu1, Bing Liu1, Lei Shu1, Philip

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.

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