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Radical-level Ideograph Encoder for RNN-based Sentiment Analysis of Chinese and Japanese
The character vocabulary can be very large in non-alphabetic languages such as Chinese and Japanese, which makes neural network models huge to process such languages.
Improve Lexicon-based Word Embeddings By Word Sense Disambiguation
To evaluate the proposed method, we compare the performance of the word embeddings trained by our proposed model, the control groups without the filter or the lexicon, and the prior works in the word similarity tasks and text classification task.
Alleviating Overfitting for Polysemous Words for Word Representation Estimation Using Lexicons
We compare the proposed neural network with the continuous bag-of-words model, the other works improving it, and the previous works estimating distributed word representations using both a lexicon and a corpus.
Fuzzy paraphrases in learning word representations with a lexicon
In this way, our approach drops the unreliable paraphrases, keeping more reliable paraphrases at the same time.
