Disambiguation of single noun translations extracted from bilingual comparable corpora

Terminology ◽  
2001 ◽  
Vol 7 (1) ◽  
pp. 63-83 ◽  
Author(s):  
Hiroshi Nakagawa
Keyword(s):  
Information Retrieval ◽  
Language Translation ◽  
Target Language ◽  
Comparable Corpora ◽  
Source Language ◽  
Bilingual Dictionary ◽  
Second Stage ◽  
Cross Language Information Retrieval ◽  
Machine Readable ◽  
Cross Language

Bilingual machine readable dictionaries are important and indispensable resources of information for cross-language information retrieval, and machine translation. Recently, these cross-language informational activities have begun to focus on specific academic or technological domains. In this paper, we describe a bilingual dictionary acquisition system which extracts translations from non-parallel but comparable corpora of a specific academic domain and disambiguates the extracted translations. The proposed method is two-fold. At the first stage, candidate terms are extracted from a Japanese and English corpus, respectively, and ranked according to their importance as terms. At the second stage, ambiguous translations are resolved by selecting the target language translation which is the nearest in rank to the source language term. Finally, we evaluate the proposed method in an experiment.

scholarly journals Effective preprocessing based neural machine translation for English to Telugu cross-language information retrieval

2021 ◽  
Vol 10 (2) ◽  
pp. 306
Author(s):  
B. N. V. Narasimha Raju ◽  
M. S. V. S. Bhadri Raju ◽  
K. V. V. Satyanarayana
Keyword(s):  
Information Retrieval ◽  
Machine Translation ◽  
Query Language ◽  
Target Language ◽  
Main Concern ◽  
Neural Machine Translation ◽  
Parallel Corpus ◽  
User Query ◽  
Cross Language Information Retrieval ◽  
Cross Language

<span id="docs-internal-guid-5b69f940-7fff-f443-1f09-a00e5e983714"><span>In cross-language information retrieval (CLIR), the neural machine translation (NMT) plays a vital role. CLIR retrieves the information written in a language which is different from the user's query language. In CLIR, the main concern is to translate the user query from the source language to the target language. NMT is useful for translating the data from one language to another. NMT has better accuracy for different languages like English to German and so-on. In this paper, NMT has applied for translating English to Indian languages, especially for Telugu. Besides NMT, an effort is also made to improve accuracy by applying effective preprocessing mechanism. The role of effective preprocessing in improving accuracy will be less but countable. Machine translation (MT) is a data-driven approach where parallel corpus will act as input in MT. NMT requires a massive amount of parallel corpus for performing the translation. Building an English - Telugu parallel corpus is costly because they are resource-poor languages. Different mechanisms are available for preparing the parallel corpus. The major issue in preparing parallel corpus is data replication that is handled during preprocessing. The other issue in machine translation is the out-of-vocabulary (OOV) problem. Earlier dictionaries are used to handle OOV problems. To overcome this problem the rare words are segmented into sequences of subwords during preprocessing. The parameters like accuracy, perplexity, cross-entropy and BLEU scores shows better translation quality for NMT with effective preprocessing.</span></span>

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