Learning Robust Word Representations for Natural Language Processing

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Language is a highly productive phenomenon; new words and expressions are constantly being invented. Current Natural Language Processing techniques have difficulty handling new words, so their performance on real-world text suffers. To address this, we develop robust models of word semantics, focusing on Deep Learning methods for learning vector word representations. Furthermore, we show improvements in parsing and translation performance using systems that incorporate these word representations.