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Many of the notable early successes occurred in the field of [[machine translation]], due especially to work at IBM Research, where successively more complicated statistical models were developed. These systems were able to take advantage of existing multilingual [[text corpus|textual corpora]] that had been produced by the [[Parliament of Canada]] and the [[European Union]] as a result of laws calling for the translation of all governmental proceedings into all official languages of the corresponding systems of government. However, most other systems depended on corpora specifically developed for the tasks implemented by these systems, which was (and often continues to be) a major limitation in the success of these systems. As a result, a great deal of research has gone into methods of more effectively learning from limited amounts of data.
Recent research has increasingly focused on [[unsupervised learning|unsupervised]] and [[semi-supervised learning|semi-supervised]] learning algorithms. Such algorithms are able to learn from data that has not been hand-annotated with the desired answers
Generally, this task is much more difficult than [[supervised learning]], and typically produces less accurate results for a given amount of input data. However, there is an enormous amount of non-annotated data available (including, among other things, the entire content of the [[World Wide Web]]), which can often make up for the inferior results. ==Software==
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