A mutually-exclusive binary cross tagging framework for joint extraction of entities and relations

Area of Science:

• Natural Language Processing
• Information Extraction
• Computational Linguistics

Background:

• Joint extraction of entities and relations from unstructured text is crucial for knowledge graph construction.
• Existing methods struggle with overlapping relational triples where entities participate in multiple relations.
• This limitation hinders comprehensive information retrieval from complex sentences.

Purpose of the Study:

• To propose a novel framework for joint extraction of overlapping entities and relational triples.
• To address the limitations of current models in handling complex overlapping relation patterns.
• To develop an end-to-end solution for more accurate information extraction.

Main Methods:

• Introduced a mutually exclusive Binary Cross Tagging (BCT) scheme.
• Developed an end-to-end BCT framework for joint extraction.
• Assigned mutually exclusive binary tags to entity tokens for cross-matching to form triples.

Main Results:

• The BCT framework demonstrated encouraging performance in F1 scores on English and Chinese datasets.
• Achieved strong performance across various overlapping patterns, particularly complex ones.
• Outperformed state-of-the-art models in handling overlapping entity and relation extraction tasks.

Conclusions:

• The proposed BCT framework effectively addresses the challenge of overlapping relational triples in joint extraction.
• This method offers a robust solution for accurate information extraction from unstructured text.
• The findings suggest a significant advancement in handling complex relational data extraction.