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arXiv · 1712.02186

Product Function Need Recognition via Semi-supervised Attention Network

Hu Xu·Sihong Xie·Lei Shu·Philip S. Yu
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Abstract

Functionality is of utmost importance to customers when they purchase products. However, it is unclear to customers whether a product can really satisfy their needs on functions. Further, missing functions may be intentionally hidden by the manufacturers or the sellers. As a result, a customer needs to spend a fair amount of time before purchasing or just purchase the product on his/her own risk. In this paper, we first identify a novel QA corpus that is dense on product functionality information \footnote{The annotated corpus can be found at \url{https://www.cs.uic.edu/~hxu/}.}. We then design a neural network called Semi-supervised Attention Network (SAN) to discover product functions from questions. This model leverages unlabeled data as contextual information to perform semi-supervised sequence labeling. We conduct experiments to show that the extracted function have both high coverage and accuracy, compared with a wide spectrum of baselines.

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Hu Xu, Sihong Xie, Lei Shu, Philip S. Yu. 2017-12-06. Product Function Need Recognition via Semi-supervised Attention Network. https://arxiv.org/abs/1712.02186

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