Controlled CNN-based Sequence Labeling for Aspect Extraction

2019/05/15 by Lei Shu, Hu Xu, Shu, Lei +3
Computer Science · #Advanced Text Analysis Techniques #Computation and Language (cs.CL) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Sentiment Analysis and Opinion Mining #Topic Modeling

paper · pdf · doi:10.48550/arxiv.1905.06407

openalex publication_date 2019/05/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

One key task of fine-grained sentiment analysis on reviews is to extract aspects or features that users have expressed opinions on. This paper focuses on supervised aspect extraction using a modified CNN called controlled CNN (Ctrl). The modified CNN has two types of control modules. Through asynchronous parameter updating, it prevents over-fitting and boosts CNN's performance significantly. This model achieves state-of-the-art results on standard aspect extraction datasets. To the best of our knowledge, this is the first paper to apply control modules to aspect extraction.

Citations

• Adam: A Method for Stochastic Optimization
• Long Short-Term Memory
• Convolutional Sequence to Sequence Learning
• Convolutional Neural Networks for Sentence Classification

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