# Multilingual Modal Sense Classification using a Convolutional Neural Network

arXiv: https://arxiv.org/abs/1608.05243 · HTML: https://mlsanity.com/paper/1608.05243/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Aug 2016.

## Built on by (most cited first, 3 of 3)

- [The Possible, the Plausible, and the Desirable: Event-Based Modality Detection for Language Processing](https://mlsanity.com/paper/2106.08037/) (2021)
- [Crude Oil-related Events Extraction and Processing: A Transfer Learning Approach](https://mlsanity.com/paper/2205.00387/) (2022)
- [MIST: a Large-Scale Annotated Resource and Neural Models for Functions of Modal Verbs in English Scientific Text](https://mlsanity.com/paper/2212.07156/) (2022)

## Abstract (arXiv)

Modal sense classification (MSC) is a special WSD task that depends on the meaning of the proposition in the modal's scope. We explore a CNN architecture for classifying modal sense in English and German. We show that CNNs are superior to manually designed feature-based classifiers and a standard NN classifier. We analyze the feature maps learned by the CNN and identify known and previously unattested linguistic features. We benchmark the CNN on a standard WSD task, where it compares favorably to models using sense-disambiguated target vectors.