In this paper, we describe TextEnt, a neural network model that learns distributed representations of entities and documents directly from a knowledge base (KB). Given a document in a KB consisting of words and entity annotations, we train our model to predict the entity that the document describes and map the document and its target entity close to each other in a continuous vector space. Our model is trained using a large number of documents extracted from Wikipedia. The performance of the proposed model is evaluated using two tasks, namely fine-grained entity typing and multiclass text classification. The results demonstrate that our model achieves state-of-the-art performance on both tasks. The code and the trained representations are made available online for further academic research.
@article{arxiv.1806.02960,
title = {Representation Learning of Entities and Documents from Knowledge Base Descriptions},
author = {Ikuya Yamada and Hiroyuki Shindo and Yoshiyasu Takefuji},
journal= {arXiv preprint arXiv:1806.02960},
year = {2018}
}