# Attention-Wrapped Hierarchical BLSTMs for DDI Extraction

arXiv: https://arxiv.org/abs/1907.13561 · HTML: https://mlsanity.com/paper/1907.13561/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jul 2019.

## Built on by (most cited first, 1 of 1)

- [RexDrug: Reliable Multi-Drug Combination Extraction through Reasoning-Enhanced LLMs](https://mlsanity.com/paper/2603.08166/) (2026)

## Abstract (arXiv)

Drug-Drug Interactions (DDIs) Extraction refers to the efforts to generate hand-made or automatic tools to extract embedded information from text and literature in the biomedical domain. Because of restrictions in hand-made efforts and their lower speed, Machine-Learning, or Deep-Learning approaches have become more popular for extracting DDIs. In this study, we propose a novel and generic Deep-Learning model which wraps Hierarchical Bidirectional LSTMs with two Attention Mechanisms that outperforms state-of-the-art models for DDIs Extraction, based on the DDIExtraction-2013 corpora. This model has obtained the macro F1-score of 0.785, and the precision of 0.80.