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.

我们的工作致力于使用一小组种子词进行无监督的方面类别检测。我们提出了一个简单的框架，通过自动增强种子词的质量和选择高质量的句子进行训练，来缓解初始种子词质量和噪声对模型性能的影响。同时，我们对方面类别检测、方面术语提取和方面术语极性联合进行训练，以进一步提高性能。大量实验证明我们的框架在标准数据集上超过了强基准结果。