# Towards a Seamless Integration of Word Senses into Downstream NLP Applications

arXiv: https://arxiv.org/abs/1710.06632 · HTML: https://mlsanity.com/paper/1710.06632/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Oct 2017.

## Built on by (most cited first, 4 of 4)

- [Language Models for Text Classification: Is In-Context Learning Enough?](https://mlsanity.com/paper/2403.17661/) (2024)
- [LMMS Reloaded: Transformer-based Sense Embeddings for Disambiguation and Beyond](https://mlsanity.com/paper/2105.12449/) (2021)
- [Multi-sense embeddings through a word sense disambiguation process](https://mlsanity.com/paper/2101.08700/) (2021)
- [Knowledge Graph Fusion for Language Model Fine-tuning](https://mlsanity.com/paper/2206.14574/) (2022)

## Abstract (arXiv)

Lexical ambiguity can impede NLP systems from accurate understanding of semantics. Despite its potential benefits, the integration of sense-level information into NLP systems has remained understudied. By incorporating a novel disambiguation algorithm into a state-of-the-art classification model, we create a pipeline to integrate sense-level information into downstream NLP applications. We show that a simple disambiguation of the input text can lead to consistent performance improvement on multiple topic categorization and polarity detection datasets, particularly when the fine granularity of the underlying sense inventory is reduced and the document is sufficiently large. Our results also point to the need for sense representation research to focus more on in vivo evaluations which target the performance in downstream NLP applications rather than artificial benchmarks.