Strategies and Challenges of Multilingual Information Retrieval on Health Forum

Abstract

Multilingual information retrieval is very important to the persons who need to consolidate information from different languages posts and forums. However, it is not an easy job to find appropriate citations for a given context, especially for citations in different languages. In this paper, we define a novel computing framework of massive posts data and user behavior data to realize multilingual information retrieval and key technologies of multilingual information retrieval. This task is very challenging because the posts data are written in different languages and there exists a language gap when matching them. To tackle this problem, we propose the multilingual posts matching technology, source information handling technology, and personalized feed or smart feed technology. We evaluate the proposed methods based on a real dataset that contains Chinese posts data and English posts data. The results demonstrate that our proposed algorithms can outperform the conventional information retrieval scheme.