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Our work addresses the problem of unsupervised Aspect Category Detectionusing a small set of seed words. Recent works have focused on learningembedding spaces for seed words and sentences to establish similarities betweensentences and aspects. However, aspect representations are limited by thequality of initial seed words, and model performances are compromised by noise.To mitigate this limitation, we propose a simple framework that automaticallyenhances the quality of initial seed words and selects high-quality sentencesfor training instead of using the entire dataset. Our main concepts are to adda number of seed words to the initial set and to treat the task of noiseresolution as a task of augmenting data for a low-resource task. In addition,we jointly train Aspect Category Detection with Aspect Term Extraction andAspect Term Polarity to further enhance performance. This approach facilitatesshared representation learning, allowing Aspect Category Detection to benefitfrom the additional guidance offered by other tasks. Extensive experimentsdemonstrate that our framework surpasses strong baselines on standard datasets.

A Self-enhancement Multitask Framework for Unsupervised Aspect Category Detection