Japanese–English cross-language information retrieval integrating query and document translation methods

Fujii, Atsushi; Ishikawa, Tetsuya

doi:10.1002/scj.20076

Cited by 18 publications

(27 citation statements)

References 35 publications

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“…In pivot method, before converting the words of a source language into the target language, source language words are firstly converted into pronunciation symbol and then converted into target language words. Pronunciation symbol is the International Phonetic Alphabet for notation of all languages [40,63] Translation helps individual to communicate in nonnative languages. But it is still very difficult to remove the language barrier.…”

Section: Back Translationmentioning

confidence: 99%

Assessing Query Translation Quality Using Back Translation in Hindi-English CLIR

Chandra¹,

Dwivedi²

2017

IJISA

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Abstract-Cross-Language Information Retrieval (CLIR) is a most demanding research area of Information Retrieval (IR) which deals with retrieval of documents different from query language. In CLIR, translation is an important activity for retrieving relevant results. Its goal is to translate query or document from one language into another language. The correct translation of the query is an essential task of CLIR because incorrect translation may affect the relevancy of retrieved results.The purpose of this paper is to compute the accuracy of query translation using the back translation for a HindiEnglish CLIR system. For experimental analysis, we used FIRE-2011 dataset to select Hindi queries. Our analysis shows that back translation can be effective in improving the accuracy of query translation of the three translators used for analysis (i.e. Google, Microsoft and Babylon). Google is found best for the purpose.

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Section: Back Translationmentioning

confidence: 99%

Assessing Query Translation Quality Using Back Translation in Hindi-English CLIR

Chandra¹,

Dwivedi²

2017

IJISA

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“…In the second step, for any pair of European word e and Japanese Katakana word j, we examine whether or not j is a transliteration of e. For this purpose, we use a transliteration method [7]. If either e or j can be transliterated into its counterpart, we extract (e,j) as a transliteration-equivalent pair.…”

Section: Extracting Synonymsmentioning

confidence: 99%

“…If p(s|t) = 0, t is not a transliteration of s. p(t) denotes the probability that t is generated as a word in the target language [7]. However, in our case we always set p(t) = 1, because our purpose is to check whether or not two given words comprise a transliteration pair.…”

Section: Extracting Synonymsmentioning

confidence: 99%

Modeling anchor text and classifying queries to enhance web document retrieval

Fujii

2008

Proceedings of the 17th International Conference on World Wide Web

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Several types of queries are widely used on the World Wide Web and the expected retrieval method can vary depending on the query type. We propose a method for classifying queries into informational and navigational types. Because terms in navigational queries often appear in anchor text for links to other pages, we analyze the distribution of query terms in anchor texts on the Web for query classification purposes. While content-based retrieval is effective for informational queries, anchor-based retrieval is effective for navigational queries. Our retrieval system combines the results obtained with the content-based and anchor-based retrieval methods, in which the weight for each retrieval result is determined automatically depending on the result of the query classification. We also propose a method for improving anchor-based retrieval. Our retrieval method, which computes the probability that a document is retrieved in response to the given query, identifies synonyms of query terms in the anchor texts on the Web and uses these synonyms for smoothing purposes in the probability estimation. We use the NTCIR test collections and show the effectiveness of individual methods and the entire Web retrieval system experimentally.

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“…It is similar to transliteration, but no phonetic elements are included. The technique bears some resemblance to query translation and transliteration research reported in Fujii and Ishikawa (2001). Fujii and Ishikawa use character-based rules to establish mapping between English characters and romanized Japanese katakana characters.…”

Section: Introductionmentioning

confidence: 99%