Abstract
Cross language information retrieval focuses on how to use the query expressed in one language to search the information expressed in another language. One of the key problems is to adopt different methods to establish bilingual semantic correspondence. In recent years, topic model has become an effective method in machine learning, information retrieval and natural language processing. This paper systematically studies the cross language retrieval model, cross language text classification method and cross language text clustering method. Without the help of cross language resources such as machine translation and bilingual dictionaries, it can effectively solve the many to many problem of Vocabulary Translation in CLIR and the problem of partial decomposition of unknown words. The experimental results on the cross language text classification evaluation corpus established in this paper show that the performance of cross language and single language text classification on the bilingual topic space constructed by this method is close to or better than that of single language classification on the original feature space, and the performance of cross language text clustering is close to or better than that of single language document clustering.
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Funding
1. This paper is supported by the Postgraduate Research and Innovation Project of Northwest Minzu University , the name of the project is “A study on the names and descriptions of the things of mongolian <qin ding li fan yuan ze li>” (project number is Yxm202009), which is the phased achievement of this project.
2. This paper is supported by “the Fundamental Research Funds for the Central Universities-A Study on the Influence of Ecological Protection Policy of Qilian Mountains on Surrounding Herdsmen from the Perspective of Ecological Safety”, which number is 31920190133.
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© 2022 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Wu, Rh., Cao, Yj. (2022). Research on Intelligent Retrieval Model of Multilingual Text Information in Corpus. In: Liu, S., Ma, X. (eds) Advanced Hybrid Information Processing. ADHIP 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 416. Springer, Cham. https://doi.org/10.1007/978-3-030-94551-0_3
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DOI: https://doi.org/10.1007/978-3-030-94551-0_3
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