A Self-enhancement Multitask Framework for Unsupervised Aspect Category Detection
This work addresses aspect detection in sentiment analysis for NLP applications, but it is incremental as it builds on existing methods with multitask learning and noise reduction.
The paper tackles unsupervised Aspect Category Detection by proposing a framework that enhances initial seed words and selects high-quality sentences for training, while jointly training with related tasks like Aspect Term Extraction and Polarity. The result is improved performance, surpassing strong baselines on standard datasets, though no specific numbers are provided.
Our work addresses the problem of unsupervised Aspect Category Detection using a small set of seed words. Recent works have focused on learning embedding spaces for seed words and sentences to establish similarities between sentences and aspects. However, aspect representations are limited by the quality of initial seed words, and model performances are compromised by noise. To mitigate this limitation, we propose a simple framework that automatically enhances the quality of initial seed words and selects high-quality sentences for training instead of using the entire dataset. Our main concepts are to add a number of seed words to the initial set and to treat the task of noise resolution as a task of augmenting data for a low-resource task. In addition, we jointly train Aspect Category Detection with Aspect Term Extraction and Aspect Term Polarity to further enhance performance. This approach facilitates shared representation learning, allowing Aspect Category Detection to benefit from the additional guidance offered by other tasks. Extensive experiments demonstrate that our framework surpasses strong baselines on standard datasets.