Connecting Language and Knowledge with Heterogeneous Representations for Neural Relation Extraction

March 25, 2019 · Entered Twilight · 🏛 North American Chapter of the Association for Computational Linguistics

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Repo contents: .gitignore, LICENSE, README.md, bilstm.py, complex_hrere.py, config.py, create_kg.py, data, eval.py, final_plot.py, get_embeddings.py, log, model.py, model_param_space.py, plot, preprocess.py, real_hrere.py, task.py, utils

Authors Peng Xu, Denilson Barbosa arXiv ID 1903.10126 Category cs.CL: Computation & Language Citations 42 Venue North American Chapter of the Association for Computational Linguistics Repository https://github.com/billy-inn/HRERE ⭐ 69 Last Checked 1 month ago

Abstract

Knowledge Bases (KBs) require constant up-dating to reflect changes to the world they represent. For general purpose KBs, this is often done through Relation Extraction (RE), the task of predicting KB relations expressed in text mentioning entities known to the KB. One way to improve RE is to use KB Embeddings (KBE) for link prediction. However, despite clear connections between RE and KBE, little has been done toward properly unifying these models systematically. We help close the gap with a framework that unifies the learning of RE and KBE models leading to significant improvements over the state-of-the-art in RE. The code is available at https://github.com/billy-inn/HRERE.