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Word embeddings learned on unlabeled data are a popular tool in semantics, but may not capture the desired semantics. We propose a new learning objective that in-corporates both a neural language model objective (Mikolov et al., 2013) and prior knowledge from semantic resources to learn improved lexical semantic embed-dings. We demonstrate that our embed-dings improve over those learned solely on raw text in three settings: language mod-eling, measuring semantic similarity, and predicting human judgements.
Yu et al. (Wed,) studied this question.