Controlled CNN-based Sequence Labeling for Aspect Extraction

05/15/2019
by   Lei Shu, et al.
0

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
05/11/2018

Double Embeddings and CNN-based Sequence Labeling for Aspect Extraction

One key task of fine-grained sentiment analysis of product reviews is to...
research
09/19/2017

Aspect-Based Sentiment Analysis Using a Two-Step Neural Network Architecture

The World Wide Web holds a wealth of information in the form of unstruct...
research
12/23/2016

Supervised Opinion Aspect Extraction by Exploiting Past Extraction Results

One of the key tasks of sentiment analysis of product reviews is to extr...
research
09/17/2021

SentiPrompt: Sentiment Knowledge Enhanced Prompt-Tuning for Aspect-Based Sentiment Analysis

Aspect-based sentiment analysis (ABSA) is an emerging fine-grained senti...
research
01/29/2022

A Simple Information-Based Approach to Unsupervised Domain-Adaptive Aspect-Based Sentiment Analysis

Aspect-based sentiment analysis (ABSA) is a fine-grained sentiment analy...
research
08/08/2017

Mining fine-grained opinions on closed captions of YouTube videos with an attention-RNN

Video reviews are the natural evolution of written product reviews. In t...
research
11/30/2015

Aspect-based Opinion Summarization with Convolutional Neural Networks

This paper considers Aspect-based Opinion Summarization (AOS) of reviews...

Please sign up or login with your details

Forgot password? Click here to reset