Search results
Results From The WOW.Com Content Network
A rendition of the Vauquois triangle, illustrating the various approaches to the design of machine translation systems.. The direct, transfer-based machine translation and interlingual machine translation methods of machine translation all belong to RBMT but differ in the depth of analysis of the source language and the extent to which they attempt to reach a language-independent ...
Neural machine translation (NMT) is an approach to machine translation that uses an artificial neural network to predict the likelihood of a sequence of words, typically modeling entire sentences in a single integrated model.
GNMT improved on the quality of translation by applying an example-based (EBMT) machine translation method in which the system learns from millions of examples of language translation. [2] GNMT's proposed architecture of system learning was first tested on over a hundred languages supported by Google Translate. [ 2 ]
It consisted of IBM computer manuals, transcribed telephone conversations, and other texts, together containing over 4.5 million words of American English, annotated using both part-of-speech tagging and syntactic bracketing. [6] Japanese sentence corpora were analyzed and a pattern of log-normality was found in relation to sentence length. [7]
Spark NLP for Healthcare is a commercial extension of Spark NLP for clinical and biomedical text mining. [10] It provides healthcare-specific annotators, pipelines, models, and embeddings for clinical entity recognition, clinical entity linking, entity normalization, assertion status detection, de-identification, relation extraction, and spell checking and correction.
The origins of machine translation can be traced back to the work of Al-Kindi, a ninth-century Arabic cryptographer who developed techniques for systemic language translation, including cryptanalysis, frequency analysis, and probability and statistics, which are used in modern machine translation. [3]
Moses is a statistical machine translation engine that can be used to train statistical models of text translation from a source language to a target language, developed by the University of Edinburgh. [2] Moses then allows new source-language text to be decoded using these models to produce automatic translations in the target language.
Example-based machine translation (EBMT) is a method of machine translation often characterized by its use of a bilingual corpus with parallel texts as its main knowledge base at run-time. It is essentially a translation by analogy and can be viewed as an implementation of a case-based reasoning approach to machine learning .