# Partially-supervised Mention Detection

arXiv: https://arxiv.org/abs/1908.09507 · HTML: https://mlsanity.com/paper/1908.09507/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Aug 2019.

## Built on by (most cited first, 5 of 5)

- [Graph Refinement for Coreference Resolution](https://mlsanity.com/paper/2203.16574/) (2022)
- [Transformers as Graph-to-Graph Models](https://mlsanity.com/paper/2310.17936/) (2023)
- [A Structured Span Selector](https://mlsanity.com/paper/2205.03977/) (2022)
- [Nested Named Entity Recognition as Single-Pass Sequence Labeling](https://mlsanity.com/paper/2505.16855/) (2025)
- [ToMMeR -- Efficient Entity Mention Detection from Large Language Models](https://mlsanity.com/paper/2510.19410/) (2025)

## Abstract (arXiv)

Learning to detect entity mentions without using syntactic information can be useful for integration and joint optimization with other tasks. However, it is common to have partially annotated data for this problem. Here, we investigate two approaches to deal with partial annotation of mentions: weighted loss and soft-target classification. We also propose two neural mention detection approaches: a sequence tagging, and an exhaustive search. We evaluate our methods with coreference resolution as a downstream task, using multitask learning. The results show that the recall and F1 score improve for all methods.