Difference between revisions of "Machine Translation"
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* '''Rule-based''', includes transfer-based, interlingual, and dictionary-based translations | * '''Rule-based''', includes transfer-based, interlingual, and dictionary-based translations | ||
* '''Statistical''', generates translations by using statistical methods on bilingual corpora | * '''Statistical''', generates translations by using statistical methods on bilingual corpora | ||
| − | * '''Example Based''', | + | * '''Example Based''', basically "translation by analogy" |
* '''Hybrid''', combines aspects of both rule-based and statistical machine translation | * '''Hybrid''', combines aspects of both rule-based and statistical machine translation | ||
Revision as of 22:53, 29 September 2010
Summary
Machine Translation (or MT for short) is a task in the field of computational linguistics which looks at translating some input in one natural language, in the form of text or speech, into another natural language with computer software.
Common Approaches
Some common approaches to Machine Translation include the following:
- Rule-based, includes transfer-based, interlingual, and dictionary-based translations
- Statistical, generates translations by using statistical methods on bilingual corpora
- Example Based, basically "translation by analogy"
- Hybrid, combines aspects of both rule-based and statistical machine translation
Challenges / Issues
Some major challenges in machine translation include handling named entities, word sense disambiguation, and handling special cases like idioms.
