# Semi Supervised Preposition-Sense Disambiguation using Multilingual Data

arXiv: https://arxiv.org/abs/1611.08813 · HTML: https://mlsanity.com/paper/1611.08813/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Nov 2016.

## Built on by (most cited first, 2 of 2)

- [Text-based NP Enrichment](https://mlsanity.com/paper/2109.12085/) (2021)
- [Tapping BERT for Preposition Sense Disambiguation](https://mlsanity.com/paper/2111.13972/) (2021)

## Abstract (arXiv)

Prepositions are very common and very ambiguous, and understanding their sense is critical for understanding the meaning of the sentence. Supervised corpora for the preposition-sense disambiguation task are small, suggesting a semi-supervised approach to the task. We show that signals from unannotated multilingual data can be used to improve supervised preposition-sense disambiguation. Our approach pre-trains an LSTM encoder for predicting the translation of a preposition, and then incorporates the pre-trained encoder as a component in a supervised classification system, and fine-tunes it for the task. The multilingual signals consistently improve results on two preposition-sense datasets.