Learning Distributed Representations of Texts and Entities from Knowledge Base

Understand

We describe a neural network model that jointly learns distributed representations of texts and knowledge base (KB) entities.

• Given a text in the KB, we train our proposed model to predict entities that are relevant to the text.
• Our model is designed to be generic with the ability to address various NLP tasks with ease.
• We train the model using a large corpus of texts and their entity annotations extracted from Wikipedia.