Building Language Model Using Error-Correcting Output Codes
نویسندگان
چکیده
1. Because the length of code is much less than the number of words, by casting the problem of predicting word to predicting each bit, we can reduce the size of the problem dramatically. Accordingly, the amount of data needed for training can be reduced. 2. By using error correcting output coding, it is possible that we can get better generalization performance on test data. 3. The distributed representation of word is more biologically plausible. 4. It is possible to encode the semantic and syntactic function of word directly into the codes.
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