Unsupervised Aspect Term Extraction with B-LSTM & CRF using Automatically Labelled Datasets

• Athanasios Giannakopoulos ^[1] ; Claudiu Musat ^[1] ; Andreea Hossmann ^[1] ; Michael Baeriswyl ^[1]
  1. [1] Artificial Intelligence and Machine Learning Group — Swisscom AG
• Localización: 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis WASSA 2017: Proceedings of the Workshop / Alexandra Balahur Dobrescu (ed. lit.), Saif M. Mohammad (ed. lit.), Erik van der Goot (ed. lit.), 2017, ISBN 978-1-945626-95-1, págs. 180-188
• Idioma: inglés
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• Resumen

  • Aspect Term Extraction (ATE) identifies opinionated aspect terms in texts and is one of the tasks in the SemEval As- pect Based Sentiment Analysis (ABSA) contest. The small amount of avail- able datasets for supervised ATE and the costly human annotation for aspect term labelling give rise to the need for unsu- pervised ATE. In this paper, we introduce an architecture that achieves top-ranking performance for supervised ATE. More- over, it can be used efficiently as fea- ture extractor and classifier for unsuper- vised ATE. Our second contribution is a method to automatically construct datasets for ATE. We train a classifier on our auto- matically labelled datasets and evaluate it on the human annotated SemEval ABSA test sets. Compared to a strong rule-based baseline, we obtain a dramatically higher F-score and attain precision values above 80%. Our unsupervised method beats the supervised ABSA baseline from SemEval, while preserving high precision scores.