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The tasks of aspect identification and term extraction remain challenging innatural language processing. While supervised methods achieve high scores, itis hard to use them in real-world applications due to the lack of labelleddatasets. 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 novelunsupervised neural network with convolutional multi-attention mechanism, thatallows extracting pairs (aspect, term) simultaneously, and demonstrate theeffectiveness on the real-world dataset. We apply a special loss aimed toimprove the quality of multi-aspect extraction. The experimental studydemonstrates, what with this loss we increase the precision not only on thisjoint setting but also on aspect prediction only.

Unsupervised Neural Aspect Search with Related Terms Extraction