Translation approaches in Cross Language Information Retrieval

Raju, B. N. V. Narasimha; Raju, M.S.V.S. Bhadri; Satyanarayana, K.

doi:10.1109/iccct2.2014.7066750

Cited by 5 publications

(3 citation statements)

References 20 publications

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“…These statistical representations contain particulars such as interaction between sources, target texts and are build by using supervised and unsupervised algorithms. The statistical [11] help the system to obtain the perfect translation for the text while translation is performed.…”

Section: Statistical Machine Translation (Smt)mentioning

confidence: 99%

A Survey on Hybrid Machine Translation

Anugu¹,

Ramesh²

2020

E3S Web Conf.

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Machine translation has gradually developed in past 1940’s.It has gained more and more attention because of effective and efficient nature. As it makes the translation automatically without the involvement of human efforts. The distinct models of machine translation along with “Neural Machine Translation (NMT)” is summarized in this paper. Researchers have previously done lots of work on Machine Translation techniques and their evaluation techniques. Thus, we want to demonstrate an analysis of the existing techniques for machine translation including Neural Machine translation, their differences and the translation tools associated with them. Now-a-days the combination of two Machine Translation systems has the full advantage of using features from both the systems which attracts in the domain of natural language processing. So, the paper also includes the literature survey of the Hybrid Machine Translation (HMT).

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Section: Statistical Machine Translation (Smt)mentioning

confidence: 99%

A Survey on Hybrid Machine Translation

Anugu¹,

Ramesh²

2020

E3S Web Conf.

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“…A major challenge in CLIR is to bridge the language gap between query and documents. Query translation is now serving as a major cross-lingual mechanism in current CLIR systems [5], [40], [70] as shown in figure2. CLIR search engines enable users to retrieve content in a language different from language used to formulate the query.…”

Section: Query Translation Approachmentioning

confidence: 99%

A Survey on Cross Language Information Retrieval

Dwivedi¹,

Chandra²

2016

IJCI

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Now a days, number of Web Users accessing information over Internet is increasing day by day. A huge amount of information on Internet is available in different language that can be access by anybody at any time. Information Retrieval (IR) deals with finding useful information from a large collection of unstructured, structured and semi-structured data. Information Retrieval can be classified into different classes such as monolingual information retrieval, cross language information retrieval and multilingual information retrieval (MLIR) etc. In the current scenario, the diversity of information and language barriers are the serious issues for communication and cultural exchange across the world. To solve such barriers, cross language information retrieval (CLIR) system, are nowadays in strong demand. CLIR refers to the information retrieval activities in which the query or documents may appear in different languages. This paper takes an overview of the new application areas of CLIR and reviews the approaches used in the process of CLIR research for query and document translation. Further, based on available literature, a number of challenges and issues in CLIR have been identified and discussed.

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“…The English content on web has shrunk from 39 to 27% in last decade (Narasimha Raju and Bhadri Raju, 2015). On other side web content for languages like Chinese, Japanese, Hindi, Arabic etc.…”

Section: Introductionmentioning

confidence: 99%

English-Hindi Cross Language Information Retrieval System: Query Perspective

Bajpai¹,

Verma²,

Abbas³

2018

Journal of Computer Science

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The abundance of multilingual content on internet other than English gives an urge to develop information retrieval system that can cross language boundaries. Such cross lingual information retrieval systems will bridge this language gap and allow user to ask a query in regional language and retrieve relevant documents in a different language. The problem of finding relevant document in language different from source language is the most challenging application of any cross lingual information retrieval. This paper discusses the development process of complete English to Hindi cross language information retrieval system along with the contribution of individual components to the system. The main focus of this paper is to discuss how optimization is done to our disambiguation approach, which we named as 'Two level Disambiguation method'. The experimental results obtained affirm that the addition of a component 'Analyzer' to our CLIR architecture increases the efficiency of our proposed disambiguation algorithm.

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Translation approaches in Cross Language Information Retrieval

Cited by 5 publications

References 20 publications

A Survey on Hybrid Machine Translation

A Survey on Hybrid Machine Translation

A Survey on Cross Language Information Retrieval

English-Hindi Cross Language Information Retrieval System: Query Perspective

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