This information might provide us more insight and understanding into MLIR merging. In addition to the effectiveness, through the merge model generated by FRank, our method can further identify key factors that influence the merging process. The experimental results show that our proposed method can significantly improve merging quality on two different types of datasets. Moreover, several merging methods are also carried out for a comparison, including traditional merging methods, the 2-step merging strategy, and the merging method based on logistic regression. In our experiments, three test collections for the task of crosslingual information retrieval (CLIR) in NTCIR3, 4, and 5 are employed to assess the performance of our proposed method. To the best of our knowledge, this practice is the first attempt to use a learning-based ranking algorithm to construct a merge model for MLIR merging. After the feature extraction, we then use the FRank ranking algorithm to construct a merge model. These features are mainly extracted from three levels: query, document, and translation. To conduct the learning approach, we present a number of features that may influence the MLIR merging process. This paper proposes a learning approach for the merging process in multilingual information retrieval (MLIR). Performs better than traditional merging methods. The results prove that even in this scenario, 2-step RSV In order to deal with queries with partial word-level alignment. Because this is not always possible, we have researched on a mixed model. The proposed method requires query vocabulary alignment: given a word for a given query, we must know the translation or translations This approach, which we call 2-step RSV (RSV: Retrieval Status Value), is based on the re-indexing of the retrieval documentsĪccording to the query vocabulary, and it performs noticeably better than traditional methods.

In this paper, we propose an approach to obtain a single list of relevant documents for CLIR systems driven by query translation. Must merge them in order to provide a single list of retrieved articles. After searching in these corpora and obtaining a result list per language, we In this way, we obtainĪs many different collections as languages. Thus, this approach divides documents according to language. Information retrieval process per language. The user query is translated for each language present in the multilingual collection in order to compute an independent monolingual Satisfactory results are obtained by 0.5 threshold value.Ī usual strategy to implement CLIR (Cross-Language Information Retrieval) systems is the so-called query translation approach. In our research, various models have been analyzed in the opinion of the document matching threshold value. Syntactic and semantic analysis of legal documents followed by query processing helps in retrieving inferences from the knowledge base to answer the query. This system is developed in an amenable way to retrieve the relevant subsection in accordance with the legal terminology embedded inquiry entered by the user. Legal QA system is developed with the help of two computational areas-Natural Language Processing and Information Retrieval. In this chapter, a sophisticated legal Question-Answer (QA) system is developed specifically for law domain which will be able to retrieve the relevant and best suitable document for any specific law domain queries posted by users’. To satisfy the law stakeholders’ need, the system should be able to deal with the semantics of law domain content. Intelligent and instinctive legal document subsection information retrieval system is much needed for the appropriate jurisprudential system. Finally, we summarize the overview and make a further outlook about CLIR. We also introduce some of the latest algorithms and techniques, such as word embedding and query expansion. Besides, we also discuss the main challenges in cross-language information retrieval. This paper introduces the translation techniques such as query translation, document translation and machine translation used in cross-language information retrieval, and analyzes their advantages and disadvantages. Cross-language information retrieval (CLIR), as a sub-field of information retrieval, can meet the purpose of users to retrieve information in other languages which are different from the queries, thus greatly improving the ability of users to retrieve multi-language documents. It demands the advancement of technologies to eliminate the communication barriers among languages.

Users are no longer satisfied with obtaining information expressed only in their mother tongue, and there is a growing need to retrieve documents in other languages.

With the development of the Internet, various language resources are becoming more and more abundant.

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