# Effective Attention Modeling for Neural Relation Extraction

arXiv: https://arxiv.org/abs/1912.03832 · HTML: https://mlsanity.com/paper/1912.03832/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Dec 2019.

## Built on by (most cited first, 10 of 10)

- [Revisiting DocRED -- Addressing the False Negative Problem in Relation Extraction](https://mlsanity.com/paper/2205.12696/) (2022)
- [Deep Neural Approaches to Relation Triplets Extraction: A Comprehensive Survey](https://mlsanity.com/paper/2103.16929/) (2021)
- [RTE: A Tool for Annotating Relation Triplets from Text](https://mlsanity.com/paper/2108.08184/) (2021)
- [Improving Distantly Supervised Relation Extraction with Self-Ensemble Noise Filtering](https://mlsanity.com/paper/2108.09689/) (2021)
- [Modeling Multi-Granularity Hierarchical Features for Relation Extraction](https://mlsanity.com/paper/2204.04437/) (2022)
- [TERMinator: A system for scientific texts processing](https://mlsanity.com/paper/2209.14854/) (2022)
- [90% F1 Score in Relational Triple Extraction: Is it Real ?](https://mlsanity.com/paper/2302.09887/) (2023)
- [tagE: Enabling an Embodied Agent to Understand Human Instructions](https://mlsanity.com/paper/2310.15605/) (2023)
- [A Hierarchical Entity Graph Convolutional Network for Relation Extraction across Documents](https://mlsanity.com/paper/2108.09505/) (2021)
- [Do the Benefits of Joint Models for Relation Extraction Extend to Document-level Tasks?](https://mlsanity.com/paper/2310.00696/) (2023)

## Abstract (arXiv)

Relation extraction is the task of determining the relation between two entities in a sentence. Distantly-supervised models are popular for this task. However, sentences can be long and two entities can be located far from each other in a sentence. The pieces of evidence supporting the presence of a relation between two entities may not be very direct, since the entities may be connected via some indirect links such as a third entity or via co-reference. Relation extraction in such scenarios becomes more challenging as we need to capture the long-distance interactions among the entities and other words in the sentence. Also, the words in a sentence do not contribute equally in identifying the relation between the two entities. To address this issue, we propose a novel and effective attention model which incorporates syntactic information of the sentence and a multi-factor attention mechanism. Experiments on the New York Times corpus show that our proposed model outperforms prior state-of-the-art models.