Unsupervised Neural Aspect Search with Related Terms Extraction

• URL: http://arxiv.org/abs/2005.02771v1
• Date: Wed, 6 May 2020 12:39:45 GMT
• Title: Unsupervised Neural Aspect Search with Related Terms Extraction
• Authors: Timur Sokhin, Maria Khodorchenko, and Nikolay Butakov
• Abstract summary: We present a novel unsupervised neural network with convolutional multi-attention mechanism, that allows extracting pairs (aspect, term) simultaneously. We apply a special loss aimed to improve the quality of multi-aspect extraction.
• Score: 0.3670422696827526
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The tasks of aspect identification and term extraction remain challenging in natural language processing. While supervised methods achieve high scores, it is hard to use them in real-world applications due to the lack of labelled datasets. Unsupervised approaches outperform these methods on several tasks, but it is still a challenge to extract both an aspect and a corresponding term, particularly in the multi-aspect setting. In this work, we present a novel unsupervised neural network with convolutional multi-attention mechanism, that allows extracting pairs (aspect, term) simultaneously, and demonstrate the effectiveness on the real-world dataset. We apply a special loss aimed to improve the quality of multi-aspect extraction. The experimental study demonstrates, what with this loss we increase the precision not only on this joint setting but also on aspect prediction only.

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