论文标题
无监督的神经方面搜索与相关术语提取
Unsupervised Neural Aspect Search with Related Terms Extraction
论文作者
论文摘要
在自然语言处理中，方面识别和术语提取的任务仍然具有挑战性。尽管有监督的方法达到了很高的分数，但由于缺乏标记的数据集，很难在现实世界应用中使用它们。无监督的方法在几个任务上都优于这些方法，但是提取一个方面和相应术语，尤其是在多种环境中，仍然是一个挑战。在这项工作中，我们提出了一种具有卷积多发明机制的新型无监督神经网络，该网络允许同时提取对（方面，术语），并证明对现实数据集的有效性。我们采用特殊损失，旨在提高多方面提取的质量。实验研究表明，由于这种损失，我们不仅在这种关节环境中，而且仅在方面预测上提高了精度。
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.
