Abstract

Organized relational knowledge in the form of “knowledge graphs” is important for many applications. However, the ability to populate knowledge bases with facts automatically extracted from documents has improved frustratingly slowly. This paper simultaneously addresses two issues that have held back prior work. We first propose an effective new model, which combines an LSTM sequence model with a form of entity position-aware attention that is better suited to relation extraction. Then we build TACRED, a large (119,474 examples) supervised relation extraction dataset obtained via crowdsourcing and targeted towards TAC KBP relations. The combination of better supervised data and a more appropriate high-capacity model enables much better relation extraction performance. When the model trained on this new dataset replaces the previous relation extraction component of the best TAC KBP 2015 slot filling system, its F1 score increases markedly from 22.2% to 26.7%.

Highlights

  • A basic but highly important challenge in natural language understanding is being able to populate a knowledge base with relational facts contained in a piece of text

  • A system can produce a knowledge graph of relational facts between persons, organizations, and locations in the text. This task involves entity recognition, mention coreference and/or entity linking, and relation extraction; we focus on the Penner is survived by his brother, John, a copy editor at the Times, and his former wife, Times sportswriter Lisa Dillman

  • The logistic regression model was trained on approximately 2 million bootstrapped examples that are carefully tuned for TAC Knowledge Base Population (TAC KBP) slot filling evaluation

Read more

Summary

Introduction

A basic but highly important challenge in natural language understanding is being able to populate a knowledge base with relational facts contained in a piece of text. The system should extract triples, or equivalently, knowledge graph edges, such as hPenner, per:spouse, Lisa Dillmani. Combining such extractions, a system can produce a knowledge graph of relational facts between persons, organizations, and locations in the text. A system can produce a knowledge graph of relational facts between persons, organizations, and locations in the text This task involves entity recognition, mention coreference and/or entity linking, and relation extraction; we focus on the Penner is survived by his brother, John, a copy editor at the Times, and his former wife, Times sportswriter Lisa Dillman. It is a key challenge to show that NLP technology can effectively contribute to this important problem

Methods
Results
Discussion
Conclusion

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.