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 incorporates both a neural language model objective (Mikolov et al., 2013) and prior knowledge from semantic resources to learn improved lexical semantic embeddings. We demonstrate that our embeddings improve over those learned solely on raw text in three settings: language modeling, measuring semantic similarity, and predicting human judgements.
For instance, in REF , the objective function consists of both the original skip-gram objective as well as prior knowledge from semantic resources to learn improved lexical semantic embeddings.
Improving Lexical Embeddings with Semantic Knowledge
ACL
Computer Science