首页|Findings from Central Institute of Technology Provide New Insights into Machine Translation (Word Sense Disambiguation applied to Assamese-Hindi Bilingual Statistical Machine Translation)
Findings from Central Institute of Technology Provide New Insights into Machine Translation (Word Sense Disambiguation applied to Assamese-Hindi Bilingual Statistical Machine Translation)
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Investigators discuss new findings in machine translation. According to news reporting from the Central Institute of Technology by NewsRx journalists, research stated, "Word Sense Disambigua- tion (WSD) is concerned with automatically assigning the appropriate sense to an ambiguous word. WSD is an important task and plays a crucial role in many Natural Language Processing (NLP) applications." The news correspondents obtained a quote from the research from Central Institute of Technology: "A Statistical Machine Translation (SMT) system translates a source into a target language based on phrase-based statistical translation. MT plays a crucial role in a WSD system, as a source language word may be associated with multiple translations in the target language. This study aims to apply WSD to the input of the MT system to enhance the disambiguation output. Hindi WordNet was used by selecting the most frequent synonym to obtain the most accurate translation. This study also compared Naive Bayes (NB) and Decision Tree (DT) to test and build a WSD model. NB was more appropriate for the WSD task than DT when evaluated in the Weka machine learning toolkit. To the best of our knowledge, no such work has been carried out yet for the Assamese Indo-Aryan language. The applied WSD achieved better results than the baseline MT system without embedding the WSD module. The results were analyzed by linguist scholars."
Central Institute of TechnologyEmerging TechnologiesMachine LearningMachine TranslationWord Sense Disambiguation
NETL
NSTL
Eos Assoc
