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

Arora, Piyush orcid logoORCID: 0000-0002-4261-2860, Foster, Jennifer orcid logoORCID: 0000-0002-7789-4853 and Jones, Gareth J.F. orcid logoORCID: 0000-0003-2923-8365 (2013) Applying query formulation and fusion techniques for cross language news story search. In: 5th Forum on Information Retrieval Evaluation (FIRE'13), 4 -6 Dec 2013, New Delhi, India. ISBN 978-1-4503- 2830-2

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.
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Uncontrolled Keywords:Hindi Information Retrieval; Cross Language News Search; Query Translation; Query Summarization; Data Fusion; Pseudo Relevance Feedback
Subjects:Computer Science > Computational linguistics
Computer Science > Machine translating
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Institutes and Centres > ADAPT
Research Institutes and Centres > Centre for Next Generation Localisation (CNGL)
Published in: Post-Proceedings of the 4th and 5th Workshops of the Forum for Information Retrieval Evaluation (FIRE '13). . ACM. ISBN 978-1-4503- 2830-2
Official URL:http://dx.doi.org/10.1145/2701336.2701650
Copyright Information:© 2013 ACM
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:y Science Foundation Ireland (SFI) as a part of the CNGL Centre for Global Intelligent Content at DCU (Grant No: 12/CE/I2267)
ID Code:22797
Deposited On:30 Nov 2018 14:22 by Piyush Arora . Last Modified 31 Jan 2019 12:18

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