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Representation learning of words, also known as word embedding technique, is based on the distributional hypothesis that words with similar semantic meanings have similar context. The selection of context window naturally has an influence on word vectors learned. However, it is found that the word vectors are often very sensitive to the defined context window, and unfortunately there is no unified...
Extracting causal relations from natural sentences is an important issue in knowledge discovery. As a typical high level semantic problem with limited data, most systems only employ hand crafted features from various lexical semantic resources because it may generate very robust feature to support classification. However, human summarized knowledge is limited and there are more information in unlabeled...
The compositional distributional semantic models (cDSMs) aim to use numerical vectors to represent the meaning of complex language expressions. cDSMs are usually trained using single training target, either from the basic DSM or a pseudo gold standard. In this paper, a new regularized training approach that integrates multiple training targets is proposed to improve semantic composition models. The...
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