2021 IJCNLP IJCNLP 2021

Three Sentences Are All You Need: Local Path Enhanced Document Relation Extraction

Abstract

AbstractDocument-level Relation Extraction (RE) is a more challenging task than sentence RE as it often requires reasoning over multiple sentences. Yet, human annotators usually use a small number of sentences to identify the relationship between a given entity pair. In this paper, we present an embarrassingly simple but effective method to heuristically select evidence sentences for document-level RE, which can be easily combined with BiLSTM to achieve good performance on benchmark datasets, even better than fancy graph neural network based methods. We have released our code at https://github.com/AndrewZhe/Three-Sentences-Are-All-You-Need.

🌉 Interdisciplinary Bridge — Artificial Intelligence and Deep Learning and Machine Learning
🧭 Keyword Pioneer — evidence sentence selection
🐣 Hot Topic Early Bird — document-level relation extraction
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio