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Aspect Term Extraction (ATE) detects opinionated aspect terms in sentences ortext spans, with the end goal of performing aspect-based sentiment analysis.The small amount of available datasets for supervised ATE and the fact thatthey cover only a few domains raise the need for exploiting other data sourcesin new and creative ways. Publicly available review corpora contain a plethoraof opinionated aspect terms and cover a larger domain spectrum. In this paper,we first propose a method for using such review corpora for creating a newdataset for ATE. Our method relies on an attention mechanism to selectsentences that have a high likelihood of containing actual opinionated aspects.We thus improve the quality of the extracted aspects. We then use theconstructed dataset to train a model and perform ATE with distant supervision.By evaluating on human annotated datasets, we prove that our method achieves asignificantly improved performance over various unsupervised and supervisedbaselines. Finally, we prove that sentence selection matters when it comes tocreating new datasets for ATE. Specifically, we show that, using a set ofselected sentences leads to higher ATE performance compared to using the wholesentence set.

Dataset Construction via Attention for Aspect Term Extraction with Distant Supervision