# Joint Extraction of Entities and Relations Based on a Novel Decomposition Strategy Source: [Joint Extraction of Entities and Relations Based on a Novel Decomposition Strategy]() Collection index: [llms.txt]() Data updated: 2026-01-04T04:08:32.829Z Authors: Bowen Yu, Zhenyu Zhang, Xiaobo Shu, Yubin Wang, Tingwen Liu, Bin Wang, Sujian Li Published: 2019-09-10T04:08:10.000Z ## Abstract Joint extraction of entities and relations aims to detect entity pairs along with their relations using a single model. Prior work typically solves this task in the extract-then-classify or unified labeling manner. However, these methods either suffer from the redundant entity pairs, or ignore the important inner structure in the process of extracting entities and relations. To address these limitations, in this paper, we first decompose the joint extraction task into two interrelated subtasks, namely HE extraction and TER extraction. The former subtask is to distinguish all head-entities that may be involved with target relations, and the latter is to identify corresponding tail-entities and relations for each extracted head-entity. Next, these two subtasks are further deconstructed into several sequence labeling problems based on our proposed span-based tagging scheme, which are conveniently solved by a hierarchical boundary tagger and a multi-span decoding algorithm. Owing to the reasonable decomposition strategy, our model can fully capture the semantic interdependency between different steps, as well as reduce noise from irrelevant entity pairs. Experimental results show that our method outperforms previous work by 5.2%, 5.9% and 21.5% (F1 score), achieving a new state-of-the-art on three public datasets ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Relation Triple Extraction on NYT single triplet version (test)](): Relation Triple Extraction; NYT single triplet version (test); Precision: 53.8. - [Joint Entity and Relation Extraction on NYT whole span annotation (test)](): Joint Entity and Relation Extraction; NYT whole span annotation (test); Precision: 85.5. - [Joint Entity and Relation Extraction on WebNLG whole span annotation (test)](): Joint Entity and Relation Extraction; WebNLG whole span annotation (test); Precision: 84.3. - [Joint Entity and Relation Extraction on NYT\* last word annotation (test)](): Joint Entity and Relation Extraction; NYT\* last word annotation (test); Precision: 84.9. - [Joint Entity and Relation Extraction on WebNLG last word annotation (test)](): Joint Entity and Relation Extraction; WebNLG last word annotation (test); Precision: 0.84. - [Relation Extraction on NYT (test)](): Relation Extraction; NYT (test); F1 Score: 78. - [Relation Triple Extraction on WebNLG original (test)](): Relation Triple Extraction; WebNLG original (test); F1 Score (%): 83.1. ## Original sources and implementation [Original paper]() [PDF]() [Implementation]()