# Using Similarity Measures to Select Pretraining Data for NER

arXiv: https://arxiv.org/abs/1904.00585 · HTML: https://mlsanity.com/paper/1904.00585/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Apr 2019.

## Built on by (most cited first, 15 of 15)

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- [Selecting Subsets of Source Data for Transfer Learning with Applications in Metal Additive Manufacturing](https://mlsanity.com/paper/2401.08715/) (2024)
- [Konooz: Multi-domain Multi-dialect Corpus for Named Entity Recognition](https://mlsanity.com/paper/2506.12615/) (2025)
- [Win-Win Cooperation: Bundling Sequence and Span Models for Named Entity Recognition](https://mlsanity.com/paper/2207.03300/) (2022)
- [Detecting Entities in the Astrophysics Literature: A Comparison of Word-based and Span-based Entity Recognition Methods](https://mlsanity.com/paper/2211.13819/) (2022)
- [Predicting the Success of Domain Adaptation in Text Similarity](https://mlsanity.com/paper/2106.04641/) (2021)
- [BeST -- A Novel Source Selection Metric for Transfer Learning](https://mlsanity.com/paper/2501.10933/) (2025)
- [Prior Omission of Dissimilar Source Domain(s) for Cost-Effective Few-Shot Learning](https://mlsanity.com/paper/2109.05234/) (2021)
- [Evaluating the Efficacy of Foundational Models: Advancing Benchmarking Practices to Enhance Fine-Tuning Decision-Making](https://mlsanity.com/paper/2407.11006/) (2024)

## Abstract (arXiv)

Word vectors and Language Models (LMs) pretrained on a large amount of unlabelled data can dramatically improve various Natural Language Processing (NLP) tasks. However, the measure and impact of similarity between pretraining data and target task data are left to intuition. We propose three cost-effective measures to quantify different aspects of similarity between source pretraining and target task data. We demonstrate that these measures are good predictors of the usefulness of pretrained models for Named Entity Recognition (NER) over 30 data pairs. Results also suggest that pretrained LMs are more effective and more predictable than pretrained word vectors, but pretrained word vectors are better when pretraining data is dissimilar.