2017/12/06 by Hu Xu, Sihong Xie, Xu, Hu +5
Computer Science · #Advanced Text Analysis Techniques #Computation and Language (cs.CL) #FOS: Computer and information sciences #Sentiment Analysis and Opinion Mining #Text and Document Classification Technologies #cs.CL
paper · pdf · doi:10.48550/arxiv.1712.02186
arxiv created 2017/12/06 · openalex publication_date 2017/12/06 · arxiv updated 2017/12/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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 \footnoteThe annotated corpus can be found at \urlhttps://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.