Multi-Attribute Relation Extraction (MARE): Simplifying the Application of Relation Extraction
- Resource Type
- Authors
- Albert Zündorf; Philipp Kohl; Bodo Kraft; Lars Klöser
- Source
- Proceedings of the 2nd International Conference on Deep Learning Theory and Applications.
- Subject
- FOS: Computer and information sciences
Computer Science - Machine Learning
Computer Science - Computation and Language
Binary relation
Event (computing)
Computer science
Computer Science - Artificial Intelligence
Extraction (chemistry)
Natural language understanding
Complex system
computer.software_genre
Relationship extraction
Machine Learning (cs.LG)
Computer Science - Information Retrieval
Annotation
Artificial Intelligence (cs.AI)
Use case
Data mining
Computation and Language (cs.CL)
computer
Information Retrieval (cs.IR)
- Language
Natural language understanding's relation extraction makes innovative and encouraging novel business concepts possible and facilitates new digitilized decision-making processes. Current approaches allow the extraction of relations with a fixed number of entities as attributes. Extracting relations with an arbitrary amount of attributes requires complex systems and costly relation-trigger annotations to assist these systems. We introduce multi-attribute relation extraction (MARE) as an assumption-less problem formulation with two approaches, facilitating an explicit mapping from business use cases to the data annotations. Avoiding elaborated annotation constraints simplifies the application of relation extraction approaches. The evaluation compares our models to current state-of-the-art event extraction and binary relation extraction methods. Our approaches show improvement compared to these on the extraction of general multi-attribute relations.
Preprint of short paper for the 2nd International Conference on Deep Learning Theory and Applications (2021)