Two Approaches for the Resolution of Word Mismatch Problem Caused by English Words and Foreign Words in Korean Information Retrieval

Kang, Byung-Ju; Choi, Key‐Sun

doi:10.1142/s0219427901000321

Cited by 15 publications

(27 citation statements)

References 7 publications

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“…When the query and document languages are written using the same character set, it is usually helpful to retain unknown terms with only minor changes (e.g., removal of diacritic marks) in the hope that they will match terms in the document language (as may be the case for proper names and "loan words," for example). When the query and document languages are expressed in different character sets, phonetic transliteration is needed to achieve the same effect(e.g., (Kang & Choi, 2000)). …”

Section: Clir Techniquesmentioning

confidence: 99%

User-assisted query translation for interactive cross-language information retrieval

Oard

He²,

Wang

2008

Information Processing & Management

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Interactive Cross-Language Information Retrieval (CLIR), a process in which searcher and system collaborate to find documents that satisfy an information need regardless of the language in which those documents are written, calls for designs in which synergies between searcher and system can be leveraged so that the strengths of one can cover weaknesses of the other. This paper describes an approach that employs user-assisted query translation to help searchers better understand the system's operation. Supporting interaction and interface designs are introduced, and results from three user studies are presented. The results indicate that that experienced searchers presented with this new system evolve new search strategies that make effective use of the new capabilities, that they achieve retrieval effectiveness comparable to results obtained using fully automatic techniques, and that reported satisfaction with support for cross-language searching increased. The paper concludes with a description of a freely available interactive CLIR system that incorporates lessons learned from this research.

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Section: Clir Techniquesmentioning

confidence: 99%

User-assisted query translation for interactive cross-language information retrieval

Oard

He²,

Wang

2008

Information Processing & Management

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“…Test Set I (Lee et al, 1998) is composed of 1,650 E-K pairs. Since, the test set was used as a common testbed for (Lee et al, 1998;Kim et al, 1999;Kang et al, 2000;Kang et al, 2001), we use them as a testbed for comparison between our method and other methods. For comparison, 1,500 pairs are used as training data for other methods and 150 pairs are used as test data for our method and other methods.…”

Section: Methodsmentioning

confidence: 99%

“…For example, one of the possible alignment for an English word 'board' and its pronunciation '/B AO R D/' 4 is as follows. For automatic EPU-P alignment, we used the modified version of Kang's E-K alignment algorithm (Kang et al, 2000;Kang et al, 2001). It is based on Covington's algorithm (Covington, 1996).…”

Section: Epu-to-phoneme Alignmentmentioning

confidence: 99%

“…8 C4.5 is one of the popular method for recognizing boundary of chunks. Unlike Kang et al, (2000)'s method, We use 90% of EPU-P aligned data as training data and 10% of those as test data. Our 'Chunking EPU' module shows 91.7% precision.…”

Section: Estimating Pronunciationmentioning

confidence: 99%

“…Method C.A W.A [Lee et al, 1998] 69.3% 40.7% 9 [Kim et al, 1999] 79.0% 35.1% [Kang et al, 2000] 78.1% 37.6% Our method 90.82% 56.0% Table 7 Comparison test results for Test set I Method C.A W.A [Kang et al, 2000[Kang et al, , 2001 81.8% 48.7% Our method 92.86% 63.0% Table 8 Comparison test results for Test set II. In the tables our method shows higher performance especially in W.A.…”

Section: Comparison Testmentioning

confidence: 99%

See 2 more Smart Citations

An English-Korean transliteration model using pronunciation and contextual rules

Oh¹,

Choi²

2002

Proceedings of the 19th International Conference on Computational Linguistics -

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There is increasing concern about English-Korean (E-K) transliteration recently. In the previous works, direct converting methods from English alphabets to Korean alphabets were a main research topic. In this paper, we present an E-K transliteration model using pronunciation and contextual rules. Unlike the previous works, our method uses phonetic information such as phoneme and its context. We also use word formation information such as English words of Greek origin. With them, our method shows significant performance increase about 31% in word accuracy.

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On the development of name search techniques for Arabic

Aqeel¹,

Beitzel²,

Jensen³

2006

J. Am. Soc. Inf. Sci.

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The need for effective identity matching systems has led to extensive research in the area of name search. For the most part, such work has been limited to English and other Latin-based languages. Consequently, algorithms such as Soundex and n-gram matching are of limited utility for languages such as Arabic, which has vastly different morphologic features that rely heavily on phonetic information. The dearth of work in this field is partly caused by the lack of standardized test data. Consequently, we have built a collection of 7,939 Arabic names, along with 50 training queries and 111 test queries. We use this collection to evaluate a variety of algorithms, including a derivative of Soundex tailored to Arabic (ASOUNDEX), measuring effectiveness by using standard information retrieval measures. Our results show an improvement of 70% over existing approaches.

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Two Approaches for the Resolution of Word Mismatch Problem Caused by English Words and Foreign Words in Korean Information Retrieval

Cited by 15 publications

References 7 publications

User-assisted query translation for interactive cross-language information retrieval

User-assisted query translation for interactive cross-language information retrieval

An English-Korean transliteration model using pronunciation and contextual rules

On the development of name search techniques for Arabic

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