# HAMNER: Headword Amplified Multi-span Distantly Supervised Method for Domain Specific Named Entity Recognition

arXiv: https://arxiv.org/abs/1912.01731 · HTML: https://mlsanity.com/paper/1912.01731/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Dec 2019.

## Built on by (most cited first, 7 of 7)

- [Sent2Span: Span Detection for PICO Extraction in the Biomedical Text without Span Annotations](https://mlsanity.com/paper/2109.02254/) (2021)
- [Named entity recognition architecture combining contextual and global features](https://mlsanity.com/paper/2112.08033/) (2021)
- [Win-Win Cooperation: Bundling Sequence and Span Models for Named Entity Recognition](https://mlsanity.com/paper/2207.03300/) (2022)
- [DMNER: Biomedical Entity Recognition by Detection and Matching](https://mlsanity.com/paper/2306.15736/) (2023)
- [A Span-based Model for Extracting Overlapping PICO Entities from RCT Publications](https://mlsanity.com/paper/2401.06791/) (2024)
- [Enhancing AI Research Paper Analysis: Methodology Component Extraction using Factored Transformer-based Sequence Modeling Approach](https://mlsanity.com/paper/2311.03401/) (2023)
- [DistALANER: Distantly Supervised Active Learning Augmented Named Entity Recognition in the Open Source Software Ecosystem](https://mlsanity.com/paper/2402.16159/) (2024)

## Abstract (arXiv)

To tackle Named Entity Recognition (NER) tasks, supervised methods need to obtain sufficient cleanly annotated data, which is labor and time consuming. On the contrary, distantly supervised methods acquire automatically annotated data using dictionaries to alleviate this requirement. Unfortunately, dictionaries hinder the effectiveness of distantly supervised methods for NER due to its limited coverage, especially in specific domains. In this paper, we aim at the limitations of the dictionary usage and mention boundary detection. We generalize the distant supervision by extending the dictionary with headword based non-exact matching. We apply a function to better weight the matched entity mentions. We propose a span-level model, which classifies all the possible spans then infers the selected spans with a proposed dynamic programming algorithm. Experiments on all three benchmark datasets demonstrate that our method outperforms previous state-of-the-art distantly supervised methods.