Paper: Improving Lexical Embeddings with Semantic Knowledge

ACL ID P14-2089
Title Improving Lexical Embeddings with Semantic Knowledge
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2014
Authors

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.