Multilingual story link detection based on event term weighting on times and multilingual spaces

In this paper, we propose a novel approach for multilingual story link detection. Our approach uses features such as timelines and multilingual spaces for giving distinctive weights to terms that constitute linguistic representation of events. On timelines term significance is calculated by comparing term distribution of the documents on a day with that of the total document collection. Since two languages can provide more information than one language, term significance is measured on each language space, which is then used as a bridge between two languages on multilingual (here bilingual) spaces. Evaluating the method in Korean and Japanese news articles, our method achieved 14.3% improvement for monolingual story pairs, and 16.7% improvement for multilingual story pairs. By measuring the space density, the proposed weighting components are verified with a high density of the intra-event stories and a low density of the inter-events stories. This result indicates that the proposed method is helpful for multilingual story link detection.