PREDICTION OF CHANGE IN ROAD USING DOCUMENT VECTORS OF CONSTRUCTION WORKS AND COMPARISON OF THEIR TOKEN

Abstract

To predict change in road using construction works data helps mapmakes to update road maps. This paper describes the result of experiment on predicting road change using document vectors by title, items list and material list of construction works data to evaluate their effectiveness. This paper also reports comparison of their token by different extracting method. According to the results, document vectors by construction works were useful to predict road change. It’s accuracy was 0.83 and recall was 0.85 using character 2-gram of construction title through decision tree. Despite title and item list were less amount of data than material list, the former showed better results than the later. In the comparison of token, short character N-gram showed better accuracy than phrases which were extracted by delimitter, words extracted by morphological analysis and dictionary, word N-gram and clustered phrases based on word embedding.