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Set-based Similarity Measurement and Ranking Model to Identify Cases of Journalistic Text Reuse

In this paper, we describe our approach to linking news articles in a cross lingual environment, English and Hindi, as submitted for the CrossLingual Indian News Story Search (CL!NSS)[1] task at FIRE'13. In our approach, English documents are first converted to Hindi using Google Translate[2], and compared to the potential Hindi sources based on five features of the documents: title, the content of the article, unique words in content, frequent words in content, and publication date. A weighted combination of the five individual similarity scores provides an overall value for similarity. Results are promising, with a best Normalized Discounted Cumulative Gain (NDCG) to ranks 1, 5 and 10 (NDCG@1, NDCG@5, NDCG@10) of 0.6600, 0.5579, and 0.5604 respectively. These place the system in third by organization, and 5th by run.