Cross Lingual Information Retrieval (CLIR): Review of Tools, Challenges and Translation Approaches

Sharma, Vijay K; Mittal, Namita

doi:10.1007/978-81-322-2755-7_72

Cited by 13 publications

(4 citation statements)

References 9 publications

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“…A CLIR system is evaluated by using Recall and Mean Average Precision (MAP). The Recall is the fraction of relevant documents that are retrieved as shown in Equation 14. MAP for a set of queries is the mean of the average precision score of the queries.…”

Section: Experiments Results and Discussionmentioning

confidence: 99%

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An Improvement in Statistical Machine Translation in Perspective of Hindi-English Cross-Lingual Information Retrieval

Sharma¹,

Mittal²

2018

CyS

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Cross-Lingual Information Retrieval (CLIR) enables a user to query to the different language target documents. CLIR incorporates a Machine Translation (MT) technique which is in growing state for Indian languages due to the unavailability of enough resources. In this paper, a Statistical Machine Translation (SMT) system is trained on two parallel corpora separately. A large English language corpus is used for language modeling in SMT. Experiments are evaluated by using BLEU score, further, these experimental setups are used to translate the Hindi language queries for the experimental analysis of Hindi-English CLIR. Since SMT does not deal with morphological variants while the proposed Translation Induction Algorithm (TIA) deals with that, therefore, TIA outperforms the SMT systems in perspective of CLIR.

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Section: Experiments Results and Discussionmentioning

confidence: 99%

“…The direct translation approaches, DT, CT, and MT, and the indirect translation approaches, Cross-Lingual Latent Semantic Indexing (CL-LSI), Cross-Lingual Latent Dirichlet Allocation (CL-LDA), Cross-Lingual Explicit Semantic Analysis (CL-ESA) are the CLIR approaches [14,21].…”

Section: Literature Surveymentioning

confidence: 99%

An Improvement in Statistical Machine Translation in Perspective of Hindi-English Cross-Lingual Information Retrieval

Sharma¹,

Mittal²

2018

CyS

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“…We have illustrated the current tree of diverse approaches used in the context of cross-lingual information retrieval through pictorial representation in Figure 7. The approaches of query translation are also discussed in [156].…”

Section: Cross-lingual Information Retrieval (Clir)mentioning

confidence: 99%

The Use of Ontology in Retrieval: A Study on Textual, Multilingual, and Multimedia Retrieval

et al. 2019

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Web contains a vast amount of data, which are accumulated, studied, and utilized by a huge number of users on a daily basis. A substantial amount of data on the Web is available in an unstructured format, such as Web pages, books, journals, and files. Acquiring appropriate information from such humongous data has become quite challenging and a time-consuming task. Trivial keyword-based information retrieval systems highly depend on the statistics of data, thus facing word mismatch problem due to inevitable semantic and context variations of a certain word. Therefore, this marks the desperate need to organize such massive data into a structured format so that information can be easily processed in a large context by taking data semantics into account. Ontologies are not only being extensively employed in the semantic Web to store unstructured information in an organized and structured way but it has also raised the performance of diverse information retrieval approaches to a great extent. Ontological information retrieval systems retrieve data based on the similarity of semantics between the user query and the indexed data. This paper reviews modern ontology-based information retrieval methods for textual, multimedia, and cross-lingual data types. Furthermore, we compare and categorize the most recent approaches used in the above-mentioned information retrieval methods along with their major drawbacks and advantages. INDEX TERMS Ontology, text retrieval, multimedia retrieval, cross lingual retrieval.

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“…Cross-language information retrieval (CLIR) is centred upon the search for documents, reconciling queries and documents which are written in different languages [1]. In CLIR systems, translating queries is the most frequent option since they are shorter texts than the documents and their translations have limited computational costs [2][3][4]. One of the most used resources for undertaking translation processes in the field of CLIR is machine translation (MT) [5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Machine Translation Oriented to Cross-Language Information Retrieval: English-Spanish Error Analysis

Gutiérrez-Artacho

Olvera‐Lobo

Rivera-Trigueros

2019

Advances in Intelligent Systems and Computing

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The main objective of this study focuses on analysing the automatic translation of questions (intended as query inputs to a Cross-Language Information Retrieval System) and on the creation of a taxonomy of translation errors present in hybrid machine translation (HMT) systems.An analysis of translations by HMT systems was carried out. From these, there is a proposal of a type 1, 2 or 3 error taxonomy weighted according to their level of importance. Results indicate that post-editing is an essential task in the automatic translation process.

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Cross Lingual Information Retrieval (CLIR): Review of Tools, Challenges and Translation Approaches

Cited by 13 publications

References 9 publications

An Improvement in Statistical Machine Translation in Perspective of Hindi-English Cross-Lingual Information Retrieval

An Improvement in Statistical Machine Translation in Perspective of Hindi-English Cross-Lingual Information Retrieval

The Use of Ontology in Retrieval: A Study on Textual, Multilingual, and Multimedia Retrieval

Hybrid Machine Translation Oriented to Cross-Language Information Retrieval: English-Spanish Error Analysis

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