# Neural Semi-Markov Conditional Random Fields for Robust Character-Based Part-of-Speech Tagging

arXiv: https://arxiv.org/abs/1808.04208 · HTML: https://mlsanity.com/paper/1808.04208/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Aug 2018.

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

- [Joint Span Segmentation and Rhetorical Role Labeling with Data Augmentation for Legal Documents](https://mlsanity.com/paper/2302.06448/) (2023)

## Abstract (arXiv)

Character-level models of tokens have been shown to be effective at dealing with within-token noise and out-of-vocabulary words. But these models still rely on correct token boundaries. In this paper, we propose a novel end-to-end character-level model and demonstrate its effectiveness in multilingual settings and when token boundaries are noisy. Our model is a semi-Markov conditional random field with neural networks for character and segment representation. It requires no tokenizer. The model matches state-of-the-art baselines for various languages and significantly outperforms them on a noisy English version of a part-of-speech tagging benchmark dataset. Our code and the noisy dataset are publicly available at http://cistern.cis.lmu.de/semiCRF.