# An Investigation of Recurrent Neural Architectures for Drug Name Recognition

arXiv: https://arxiv.org/abs/1609.07585 · HTML: https://mlsanity.com/paper/1609.07585/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Sep 2016.

## Built on by (most cited first, 4 of 4)

- [Computer Science Named Entity Recognition in the Open Research Knowledge Graph](https://mlsanity.com/paper/2203.14579/) (2022)
- [Secondary Use of Clinical Problem List Entries for Neural Network-Based Disease Code Assignment](https://mlsanity.com/paper/2112.13756/) (2021)
- [Multicultural Name Recognition For Previously Unseen Names](https://mlsanity.com/paper/2401.12941/) (2024)
- [Leveraging large language models for SQL behavior-based database intrusion detection](https://mlsanity.com/paper/2508.05690/) (2025)

## Abstract (arXiv)

Drug name recognition (DNR) is an essential step in the Pharmacovigilance (PV) pipeline. DNR aims to find drug name mentions in unstructured biomedical texts and classify them into predefined categories. State-of-the-art DNR approaches heavily rely on hand crafted features and domain specific resources which are difficult to collect and tune. For this reason, this paper investigates the effectiveness of contemporary recurrent neural architectures - the Elman and Jordan networks and the bidirectional LSTM with CRF decoding - at performing DNR straight from the text. The experimental results achieved on the authoritative SemEval-2013 Task 9.1 benchmarks show that the bidirectional LSTM-CRF ranks closely to highly-dedicated, hand-crafted systems.