Title: Learning Distributed Representations of Texts and Entities from Knowledge Base,
Abstract: 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. We evaluated the model on three
important NLP tasks (i.e., sentence textual similarity, entity linking, and
factoid question answering) involving both unsupervised and supervised
settings. As a result, we achieved state-of-the-art results on all three of
these tasks. Our code and trained models are publicly available for further
academic research.