Проблема Разреженности Данных

Most language models used for natural lan- guage processing are continuous. However, the assumption of such kind of models is too simple to cope with data sparsity problem. Al- though …...

In this paper, we propose a recurrent neural network-based tuple sequence model (RNNTSM) that can help phrase-based translation model overcome the phrasal independence assumption. Our RNNTSM can potentially capture arbitrary …...

Statistical machine translation (SMT) suf- fers from various problems which are ex- acerbated where training data is in short supply. In this paper we address the data sparsity problem in …...

AbstractFor the task of implicit discourse relation recognition, traditional models utilizing manual features can suffer from data sparsity problem. Neural models provide a solution with distributed representations, which could encode …...

Several studies on sentence processing sug- gest that the mental lexicon keeps track of the mutual expectations between words. Current DSMs, however, represent context words as separate features, thereby loosing …...