%0 Journal Article
%T A Method for Identifying Japanese Shop and Company Names by Spatiotemporal Cleaning of Eccentrically Located Frequently Appearing Words
%A Yuki Akiyama
%A Ryosuke Shibasaki
%J Advances in Artificial Intelligence
%D 2012
%I Hindawi Publishing Corporation
%R 10.1155/2012/562604
%X We have developed a method for spatiotemporally integrating databases of shop and company information, such as from a digital telephone directory, spatiotemporally, in order to monitor dynamic urban transformations in a detailed manner. To realize this, an additional method is necessary to verify the identicalness of different instances of Japanese shop and company names that might contain fluctuations of description. In this paper, we discuss a method that utilizes an -gram model for comparing and identifying Japanese words. The processing accuracy was improved through developing various kinds of libraries for frequently appearing words, and using these libraries to clean shop and company names. In addition, the accuracy was greatly and novelty improved through the detection of those frequently appearing words that appear eccentrically across both space and time. By utilizing natural language processing (NLP), our method incorporates a novel technique for the advanced processing of spatial and temporal data. 1. Introduction Spatiotemporal changes of shop and company locations have a major effect on the vitality and attraction of urban space. It is a significant challenge to monitor these changes, quantitatively and in as detailed as manner as possible, for use in various fields including urban engineering, geography, and economics. However, it is difficult to comprehensively monitor urban spaces, because much general regional and statistical information (e.g., the population census, commercial statistics) is compiled by separate administrative or city block units. On the other hand, detailed information on shop and company locations and names can be collected using telephone directories and web information. Fortunately, this is possible in Japan, because of the availability of digital telephone directories and detailed digital maps which can monitor almost all residents and tenants in a given building. The yearly continuations and changes in tenants or residents can be monitored for a certain location, and we can integrate these data across multiple years. The same can be done for shop and company locations over multiple years, by measuring changes in shop and company names. However, this measure is not easy because of name fluctuations between different two years or different kinds of data. Therefsore, we have been developing a dataset that can monitor the time-series changes of each shop and company and a system that can develop such data as to resolve this challenge [1, 2]. This paper focuses on a particular method of name identification, pertinent
%U http://www.hindawi.com/journals/aai/2012/562604/