Enriching BERT with Knowledge Graph Embeddings for Document Classification
Computation and Language
2019-09-19 v1 Information Retrieval
Machine Learning
Abstract
In this paper, we focus on the classification of books using short descriptive texts (cover blurbs) and additional metadata. Building upon BERT, a deep neural language model, we demonstrate how to combine text representations with metadata and knowledge graph embeddings, which encode author information. Compared to the standard BERT approach we achieve considerably better results for the classification task. For a more coarse-grained classification using eight labels we achieve an F1- score of 87.20, while a detailed classification using 343 labels yields an F1-score of 64.70. We make the source code and trained models of our experiments publicly available
Cite
@article{arxiv.1909.08402,
title = {Enriching BERT with Knowledge Graph Embeddings for Document Classification},
author = {Malte Ostendorff and Peter Bourgonje and Maria Berger and Julian Moreno-Schneider and Georg Rehm and Bela Gipp},
journal= {arXiv preprint arXiv:1909.08402},
year = {2019}
}