# Statistical modality tagging from rule-based annotations and crowdsourcing

arXiv: https://arxiv.org/abs/1503.01190 · HTML: https://mlsanity.com/paper/1503.01190/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Mar 2015.

## Built on by (most cited first, 6 of 6)

- [Dealing with Disagreements: Looking Beyond the Majority Vote in Subjective Annotations](https://mlsanity.com/paper/2110.05719/) (2021)
- [On Releasing Annotator-Level Labels and Information in Datasets](https://mlsanity.com/paper/2110.05699/) (2021)
- [Modality and Negation in Event Extraction](https://mlsanity.com/paper/2109.09393/) (2021)
- [The Impact of Preference Agreement in Reinforcement Learning from Human Feedback: A Case Study in Summarization](https://mlsanity.com/paper/2311.04919/) (2023)
- [Causality Detection using Multiple Annotation Decisions](https://mlsanity.com/paper/2210.14852/) (2022)
- [It HAS to be Subjective: Human Annotator Simulation via Zero-shot Density Estimation](https://mlsanity.com/paper/2310.00486/) (2023)

## Abstract (arXiv)

We explore training an automatic modality tagger. Modality is the attitude that a speaker might have toward an event or state. One of the main hurdles for training a linguistic tagger is gathering training data. This is particularly problematic for training a tagger for modality because modality triggers are sparse for the overwhelming majority of sentences. We investigate an approach to automatically training a modality tagger where we first gathered sentences based on a high-recall simple rule-based modality tagger and then provided these sentences to Mechanical Turk annotators for further annotation. We used the resulting set of training data to train a precise modality tagger using a multi-class SVM that delivers good performance.