Conference Proceedings


Gareth J.F. Jones
Jennifer Foster
Piyush Arora


Computer Science

query translation cross language news search computational linguistics hindi information retrieval data fusion machine translating pseudo relevance feedback query summarization

Applying query formulation and fusion techniques for cross language news story search (2013)

Abstract Cross Language News story search (CLNSS) is concerned with finding documents describing the same events in documents in different languages. As well as supporting information retrieval (IR), CLNSS has other applications in mining parallel and comparable data across different languages. In this paper, we present an overview of the work carried out for our participation in the Cross Language !ndian News Story Search (CL!NSS) task at FIRE 2013. In the CL!NSS task we explored the problem of cross language news search for the English-Hindi language pair. English news stories are used as queries to seek similar news documents from Hindi news articles. Hindi being a resource-scarce language offers many challenges towards retrieving relevant news articles. We investigate and contrast translation of input queries from English to Hindi using the Google and Bing translation services. To support translation of out-of-vocabulary words we use the Google transliteration service. A key challenge of the CL!NSS task is formation of search queries from the English news articles, since they are much longer than the much shorter queries typically used in IR applications. To address this problem, we explore the use of summarization to extract a query from the input news documents, and use these summarized queries as the input to the cross language IR system. We explore the use of query expansion using pseudo relevance feedback (PRF) in the IR process, since this has been shown to be effective for cross language IR in many previous investigations. We also explore in detail the use of data fusion techniques over different sets of retrieved results obtained using diverse query formulation techniques. For the CL!NSS task our team submitted 3 main runs. The results of our best run was ranked first among official submissions based on NDCG@5 and NDCG@10 values and second for NDCG@1 values. For the 25 test queries the results of our best main run were NDCG@1 0.7400, NDCG@5 0.6809 and NDCG@10 0.7268. We present our methodology, official results and results of a number of post-task experiments that were conducted to further examine the cross language search problem. Our experiments reveal that query formulation plays a vital role in improving search results for news documents across different languages. Instead of using the complete news documents the summarized queries show better performance. Data fusion techniques also help to improve the performance of the system by boosting the rank of documents, thus improving the NDCG scores.
Collections Ireland -> Dublin City University -> Publication Type = Conference or Workshop Item
Ireland -> Dublin City University -> DCU Faculties and Centres = DCU Faculties and Schools: Faculty of Engineering and Computing: School of Computing
Ireland -> Dublin City University -> DCU Faculties and Centres = Research Initiatives and Centres: ADAPT
Ireland -> Dublin City University -> DCU Faculties and Centres = Research Initiatives and Centres: Centre for Next Generation Localisation (CNGL)
Ireland -> Dublin City University -> Status = Published
Ireland -> Dublin City University -> Subject = Computer Science: Machine translating
Ireland -> Dublin City University -> Subject = Computer Science: Computational linguistics

Full list of authors on original publication

Gareth J.F. Jones, Jennifer Foster, Piyush Arora

Experts in our system

Gareth J. F. Jones
Dublin City University
Total Publications: 297
Jennifer Foster
Dublin City University
Total Publications: 51
Piyush Arora
Dublin City University
Total Publications: 15