scholarly journals A Back Of The Book Dıctıonary Translatıon: Kutadgu Bilig

2012 ◽  
Vol Volume 7 Issue 4-I (7) ◽  
pp. 711-738
Author(s):  
Muhammet Fatih ALKAYIŞ
Keyword(s):  
Dictionary Translation

Transitive dictionary translation challenges direct dictionary translation in CLIR

2004 ◽  
Vol 40 (6) ◽  
pp. 973-988 ◽  
Author(s):  
Raija Lehtokangas ◽  
Eija Airio ◽  
Kalervo Järvelin
Keyword(s):  
Dictionary Translation

scholarly journals Bilingual (English to Bengali) Technical E- Dictionary for Aviation OOV Words

2019 ◽  
Vol 9 (2) ◽  
pp. 3777-3781
Keyword(s):  
The Other ◽  
The Core ◽  
Other Hand ◽  
Almost All ◽  
Multiple Languages ◽  
Dictionary Translation

E-dictionaries, quite common today are available for multiple languages in monolingual, bilingual and multilingual forms. In NLP they form the core of a series of tools that are used to understand words, sentences and in turn the language itself. These E-Dictionaries work well for any language domain as a whole. For almost all languages E-dictionaries are available, but once specialized technical domains are encountered these E-Dictionaries are quite useless. Aviation is one such specialized domain for which no E-Dictionary, translation or transliteration tool exist. On the other hand the need for such tools for specialized domains are increasing. The tool discussed in this paper is an attempt to bridge the gap that currently exists between English and Bengali languages.

scholarly journals A novel method for dictionary translation

2015 ◽  
Vol 47 (3) ◽  
pp. 491-514 ◽  
Author(s):  
Robert Krajewski ◽  
Henryk Rybinski ◽  
Marek Kozlowski
Keyword(s):  
Novel Method ◽  
Dictionary Translation

scholarly journals Machine-translated texts as an alternative to translated dictionaries for LIWC

2021 ◽  
Author(s):  
Peter Boot
Keyword(s):  
Machine Translation ◽  
Text Analysis ◽  
Analysis Program ◽  
Word Count ◽  
Alternative Approach ◽  
Linguistic Inquiry ◽  
Better Than ◽  
Dictionary Translation

Linguistic Inquiry and Word Count (LIWC) is a text analysis program developed by James Pennebaker and colleagues. At the basis of LIWC is a dictionary that assigns words to categories. This dictionary is specific to English. Researchers who want to use LIWC on non-English texts have typically relied on translations of the dictionary into the language of the texts. Dictionary translation, however, is a labour-intensive procedure. In this paper, we investigate an alternative approach: to use Machine Translation (MT) to translate the texts that must be analysed into English, and then use the English dictionary to analyse the texts. We test several LIWC versions, languages and MT engines, and consistently find the machine-translated text approach performs better than the translated-dictionary approach. We argue that for languages for which effective MT technology is available, there is no need to create new LIWC dictionary translations.

scholarly journals Automatic RadLex coding of Chinese structured radiology reports based on text similarity ensemble

2021 ◽  
Vol 21 (S9) ◽  
Author(s):  
Yani Chen ◽  
Shan Nan ◽  
Qi Tian ◽  
Hailing Cai ◽  
Huilong Duan ◽  
...  
Keyword(s):  
Multilayer Perceptron ◽  
Levenshtein Distance ◽  
Text Similarity ◽  
Neural Machine Translation ◽  
Secondary Use ◽  
Radiology Reports ◽  
Similarity Algorithm ◽  
Negative Effect ◽  
Cross Language ◽  
Dictionary Translation

Abstract Background Standardized coding of plays an important role in radiology reports’ secondary use such as data analytics, data-driven decision support, and personalized medicine. RadLex, a standard radiological lexicon, can reduce subjective variability and improve clarity in radiology reports. RadLex coding of radiology reports is widely used in many countries, but translation and localization of RadLex in China are far from being established. Although automatic RadLex coding is a common way for non-standard radiology reports, the high-accuracy cross-language RadLex coding is hardly achieved due to the limitation of up-to-date auto-translation and text similarity algorithms and still requires further research. Methods We present an effective approach that combines a hybrid translation and a Multilayer Perceptron weighting text similarity ensemble algorithm for automatic RadLex coding of Chinese structured radiology reports. Firstly, a hybrid way to integrate Google neural machine translation and dictionary translation helps to optimize the translation of Chinese radiology phrases to English. The dictionary is made up of 21,863 Chinese–English radiological term pairs extracted from several free medical dictionaries. Secondly, four typical text similarity algorithms are introduced, which are Levenshtein distance, Jaccard similarity coefficient, Word2vec Continuous bag-of-words model, and WordNet Wup similarity algorithms. Lastly, the Multilayer Perceptron model has been used to synthesize the contextual, lexical, character and syntactical information of four text similarity algorithms to promote precision, in which four similarity scores of two terms are taken as input and the output presents whether the two terms are synonyms. Results The results show the effectiveness of the approach with an F1-score of 90.15%, a precision of 91.78% and a recall of 88.59%. The hybrid translation algorithm has no negative effect on the final coding, F1-score has increased by 21.44% and 8.12% compared with the GNMT algorithm and dictionary translation. Compared with the single similarity, the result of the MLP weighting similarity algorithm is satisfactory that has a 4.48% increase compared with the best single similarity algorithm, WordNet Wup. Conclusions The paper proposed an innovative automatic cross-language RadLex coding approach to solve the standardization of Chinese structured radiology reports, that can be taken as a reference to automatic cross-language coding.

A Seed Based Method for Dictionary Translation

2014 ◽  
pp. 415-424 ◽  
Author(s):  
Robert Krajewski ◽  
Henryk Rybiński ◽  
Marek Kozłowski
Keyword(s):  
Dictionary Translation
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