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Skip to Search Results- 11NLP
- 6Natural language processing
- 4Computational linguistics
- 3Natural Language Processing
- 3Transliteration
- 2Alignments
- 1Bergsma, Shane A
- 1Bhargava, Aditya
- 1Campbell, Hazel V
- 1Elbarkouky, Mohamed
- 1Gharaat, Mohamad Ali
- 1Hauer, Bradley
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Fall 2011
Grapheme-to-phoneme conversion (G2P) and machine transliteration are important tasks in natural language processing. Supplemental data can often help resolve difficult ambiguities: existing transliterations of the same word can help choose among a G2P system’s candidate output transcriptions;...
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Fall 2014
There is no doubt that Internet becomes one of the most important sources of information. At the same time the amount of information stored on the web and available for users becomes enormous. In order to make this information more accessible and create prospects for software to process it...
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Fall 2017
In recent years fake news has become a more serious problem. This is mainly due to the popularity of social networks, search engines and news ag- gregators that propagate fake news. Classifying news as fake is a hard problem. However it is possible to distinguish between fake and real news, by...
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Spring 2024
This work aims to address the lack of clear theoretical foundations in computational lexical semantics, the sub-field of natural language processing pertaining to computing with the meaning of words. Semantic tasks are of interest for end-user applications (e.g. contextual translation),...
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Fall 2014
Machine transliteration is important to machine translation and cross-lingual information retrieval. Previous works show that machine transliteration can benefit from supplemental phonetic transcriptions and transliterations from other languages through a re-ranking framework. In this thesis, I...
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Fall 2022
Sentence reconstruction and generation are essential applications in Natural Language Processing (NLP). Early studies were based on classic methods such as production rules and statistical models. Recently, the prevailing models typically use deep neural networks. In this study, we utilize deep...