We describe ~ statisticM technique for assigning senses to words. An instance of ~ word is assigned ;~ sense by asking a question about the context in which the word ~tppears. The qttestlou is constructed to ha, re high mutua,1 i~fformation with the word's translations.
We describe a statistical technique for assigning senses to words. An instance of a word is assigned a sense by asking a question about the context in which the word appears. The question is constructed to have high mutual information with the translation of that instance in another language. When we incorporated this method of assigning senses into our statistical machine translation system, the error rate of the system decreased by thirteen percent.
We present an overview of Candide, a system for automatic translation of French text to English text. Candide uses methods of information theory and statistics to develop a probability model of the translation process. This model, which is made to accord as closely as possible with a large body of French and English sentence pairs, is then used to generate English translations of previously unseen French sentences. This paper provides a tutorial in these methods, discussions of the training and operation of the system, and a summary of test results.
The time required for our translation system to handle a sentence of length I is a rapidly growing function of i. We describe here a method for analyzing a sentence into a series of pieces that can be translated sequentially. We show that for sentences with ten or fewer words, it is possible to decrease the translation time by 40% with almost no effect on translation accuracy. We argue that for longer sentences, the effect should be more dramatic.
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