Controlled CNN-based Sequence Labeling for Aspect Extraction
It addresses aspect extraction for fine-grained sentiment analysis, but appears incremental as it adapts existing control modules to a new task.
This paper tackled the problem of supervised aspect extraction in sentiment analysis by proposing a modified CNN with control modules to prevent overfitting, achieving state-of-the-art results on standard datasets.
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.