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We propose learning sentiment-specific word embeddings dubbed sentiment embeddings in this paper. Existing word embedding learning algorithms typically only use the contexts of words but ignore the sentiment of texts. It is problematic for sentiment analysis because the words with similar contexts but opposite sentiment polarity, such as good and bad, are mapped to neighboring word vectors. We address...
An important factor of a corpus is its domain, usually the quality of a SMT system trained on an in-domain corpus increases by adding out-of-domain sentences to its training corpus. In this paper we have shown out-of-domain corpora may also contains sentences which are proper for improving the quality of in-domain corpus. These sentences have words and phrases that occur in indomain corpora so, their...
In this paper, we would like to introduce a new approach to recover Vietnamese text's accents. Given a Vietnamese text in which accents are lost, our goal is to seek for a recovered text that yields a best lexical probability. Using a dynamic programming approach, we first build a model of language for Vietnamese as a lexical database which gives lexical probabilities to Vietnamese sentences. Second,...
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