In this paper, we describe the approach and results of the NYUCIN team in the NTCIR-16 conference. We participated in the Data Search 2 Task, which is a shared task on ad-hoc retrieval for governmental statistical data composed of multiple subtasks. We report our work on the two subtasks we participated in: the English IR Subtask and the UI Subtask. For the IR Subtask, we explored learning-to-rank approaches based on deep learning models. Given the limited training data available for this task, we employed a transfer learning method to train a deep neural network that learns how to match web tables and news articles using data available on the Web. The official evaluation shows that our approach attained the highest score among all submitted runs across all evaluation metrics. In particular, for the nDCG@5 measure, our score of 0.246 represents a 30% improvement compared to the second-best result in NTCIR-16 Data Search 2 Task. For the experimental UI Subtask, we performed a preliminary user study to evaluate the effectiveness of the user interface of Auctus, a dataset search engine developed by our team.
Silva et al. (Tue,) studied this question.