# Span-based joint entity and relation extraction augmented with sequence tagging mechanism

arXiv: https://arxiv.org/abs/2210.12720 · HTML: https://mlsanity.com/paper/2210.12720/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 43 references that are in the map.
- First posted on arXiv 23 Oct 2022.

## Builds on (most cited first, 23 of 23)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation](https://mlsanity.com/paper/1609.08144/) (2016)
- [Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction](https://mlsanity.com/paper/1808.09602/) (2018)
- [Entity, Relation, and Event Extraction with Contextualized Span Representations](https://mlsanity.com/paper/1909.03546/) (2019)
- [DocRED: A Large-Scale Document-Level Relation Extraction Dataset](https://mlsanity.com/paper/1906.06127/) (2019)
- [End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures](https://mlsanity.com/paper/1601.00770/) (2016)
- [Higher-order Coreference Resolution with Coarse-to-fine Inference](https://mlsanity.com/paper/1804.05392/) (2018)
- [Span-based Joint Entity and Relation Extraction with Transformer Pre-training](https://mlsanity.com/paper/1909.07755/) (2019)
- [Entity-Relation Extraction as Multi-Turn Question Answering](https://mlsanity.com/paper/1905.05529/) (2019)
- [A General Framework for Information Extraction using Dynamic Span Graphs](https://mlsanity.com/paper/1904.03296/) (2019)
- [Two are Better than One: Joint Entity and Relation Extraction with Table-Sequence Encoders](https://mlsanity.com/paper/2010.03851/) (2020)
- [SemEval-2010 Task 8: Multi-Way Classification of Semantic Relations Between Pairs of Nominals](https://mlsanity.com/paper/1911.10422/) (2019)
- [Joint entity recognition and relation extraction as a multi-head selection problem](https://mlsanity.com/paper/1804.07847/) (2018)
- [A Partition Filter Network for Joint Entity and Relation Extraction](https://mlsanity.com/paper/2108.12202/) (2021)
- [Jointly Predicting Predicates and Arguments in Neural Semantic Role Labeling](https://mlsanity.com/paper/1805.04787/) (2018)
- [A Trigger-Sense Memory Flow Framework for Joint Entity and Relation Extraction](https://mlsanity.com/paper/2101.10213/) (2021)
- [End-to-end neural relation extraction using deep biaffine attention](https://mlsanity.com/paper/1812.11275/) (2018)
- [Joint Biomedical Entity and Relation Extraction with Knowledge-Enhanced Collective Inference](https://mlsanity.com/paper/2105.13456/) (2021)
- [Exploiting Entity BIO Tag Embeddings and Multi-task Learning for Relation Extraction with Imbalanced Data](https://mlsanity.com/paper/1906.08931/) (2019)
- [HySPA: Hybrid Span Generation for Scalable Text-to-Graph Extraction](https://mlsanity.com/paper/2106.15838/) (2021)

## Built on by (most cited first, 2 of 2)

- [A Comprehensive Survey on Relation Extraction: Recent Advances and New Frontiers](https://mlsanity.com/paper/2306.02051/) (2023)
- [XFormParser: A Simple and Effective Multimodal Multilingual Semi-structured Form Parser](https://mlsanity.com/paper/2405.17336/) (2024)

## Abstract (arXiv)

Span-based joint extraction simultaneously conducts named entity recognition (NER) and relation extraction (RE) in text span form. However, since previous span-based models rely on span-level classifications, they cannot benefit from token-level label information, which has been proven advantageous for the task. In this paper, we propose a Sequence Tagging augmented Span-based Network (STSN), a span-based joint model that can make use of token-level label information. In STSN, we construct a core neural architecture by deep stacking multiple attention layers, each of which consists of three basic attention units. On the one hand, the core architecture enables our model to learn token-level label information via the sequence tagging mechanism and then uses the information in the span-based joint extraction; on the other hand, it establishes a bi-directional information interaction between NER and RE. Experimental results on three benchmark datasets show that STSN consistently outperforms the strongest baselines in terms of F1, creating new state-of-the-art results.