基于Web2.0的电子商务中基于商品名的实体识别

With the emergence of Web2.0, e-commerce data are often input by different websites and users; thus, there can be many descriptions of the same commodity. This makes it very difficult for users to search for and compare commodities. This paper proposes a method for classifying commodities based on their trade names, such that each category describes an actual type of commodity. The system proposed in this paper splits the trade name into sets of keywords and subsequently classifies them based on the similarity of their keyword sets. In this paper, we propose strategies for keyword splitting, set-based classification, keyword weight setting, and related feedback. The experimental results show that the proposed method can classify commodities quickly and effectively, and the weight-setting and related-feedback strategies can effectively improve the accuracy of entity identification.