Mining collective knowledge: inferring functional labels from online review for business

With the increasing popularity of online e-commerce services, a large volume of online reviews have been constantly generated by users. In this paper, we propose to study the problem of inferring functional labels using online review text. Functional labels summarize and highlight the main characteristics of a business, which can serve as bridges between the consumption needs and the service functions. We consider two kinds of semantic similarities: lexical similarity and embedding similarity, which characterize the relatedness in two different perspectives. To measure the lexical similarity, we use the classic probabilistic ranking formula, i.e., BM25; to measure the embedding similarity, we propose an extended embedding model which can incorporate weak supervised information derived from review text. These two kinds of similarities compensate each other and capture the semantic relatedness in a more comprehensive way. We construct a test collection consisting of four different domains based on a Yelp dataset and consider multiple baseline methods for comparison. Extensive experiments have shown that the proposed methods are very effective.