KSU Systems at the NTCIR-16 Data Search2 IR Subtask

This paper describes the system and results of Team KSU work on the NTCIR-16 Data Search2 IR subtask. The documents covered by this task consist of metadata extracted from the governmental statistical data and the body of the corresponding statistical data. The metadata is characterized by the fact that its document length is short, and the main body of statistical data is almost always composed of numbers, except for titles, headers, and comments. In the previous studies on ad hoc search for statistical documents, most of the ranking methods used only the metadata of the statistical documents, and there are few methods of using the contents of the tables of statistical data. However, ranking methods using only metadata have not been able to achieve the same or better performance compared to conventional ad hoc search for text documents. Therefore, in this paper, we propose a method that employs features of the table body of statistical data and a re-ranking method based on neural network models used in neural search, and verify how much the ranking results are improved. For the features of the main body of the table, we use eight types of features, four from the main body of the table and four from the whole table. As a neural search method, we use a re-ranking method based on the scores predicted from the features obtained by BERT and MLP. The results of the experiment showed that the method combining category search and BM25 resulted in nDCG@10 of 0.314 for Japanese and that of 0.069 for English. The results showed that Japanese ranked 2nd and English 6th among all teams.