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.
Year
Venue
DocType
2017
TACL
Journal
Volume
ISSN
Citations 
abs/1705.02494
Transactions of the Association for Computational Linguistics, 5 (2017), 397-411
9
PageRank 
References 
Authors
0.54
34
4
Name
Order
Citations
PageRank
Ikuya Yamada1658.25
Hiroyuki Shindo27513.80
Hideaki Takeda317925.16
Yoshiyasu Takefuji426233.68