Fraud detection is of great importance to financial institutions. This paper is concerned with the problem of finding outliers in time series financial data using Peer Group Analysis (PGA), which is an unsupervised technique for fraud detection. The objective of PGA is to characterize the expected pattern of behavior around the target sequence in terms of the behavior of similar objects, and then to detect any difference in evolution between the expected pattern and the target. The tool has been applied to the stock market data, which has been collected from Bangladesh Stock Exchange to assess its performance in stock fraud detection. We observed PGA can detect those brokers who suddenly start selling the stock in a different way to other brokers to whom they were previously similar. We also applied t-statistics to find the deviations effectively.

Digitization of Japanese historical documents has gained much attraction in the field of humanities in Japan recently, and numbers of documents are already available in digitized text format. However, text analysis of these documents has rarely been done mainly due to the lack of natural language processing tools that can handle pre-modern Japanese. In this article, we propose a method to extract and visualize the relationships among persons from Japanese historical documents with an aid of supplementary information such as personal name and place name indices. The goal of the method is to extract dynamics of relationships among historical persons. The method utilizes locational information to obtain latent relationships among persons based on their spatial activities. The proposed method is applied to a Japanese historical chronicle written in the 12th century. Experimental results showed a strong correspondence to the known historical facts, and the results of a user survey completed by researchers of Japanese history demonstrated some potential for the method to serve as a new approach in the fields of humanities.

The opportunity to read articles and microblogs on the Web to get information is more and more increasing. However, hyperlinks to entities do not often exist in such articles, and it is a troublesome task for the reader to look it up online. In this paper, in order to make it easy to look up entity information in microblog articles, we propose a method to extract entities in Japanese microblog, and to perform entity linking which links to entity information automatically. The method consists of three phases. First, we extract named entities, such as personal names, place names, organization names, etc. from a microblog article. Next, we disambiguate the extracted entities in order to make links to correct entity information. We use Wikipedia as the source of entity information to verify the usefulness of the proposed method. In our method, we extract some Wikipedia articles related to ambiguous entities from microblog articles. Then, we extract some related entities with the ambiguous entity using word2vec. We compare Wikipedia articles of related entities and the Wikipedia articles of ambiguous entities. Finally, we get the correct Wikipedia article for each entity in the microblog article.