# Resolving Lexical Ambiguity in Tensor Regression Models of Meaning

arXiv: https://arxiv.org/abs/1408.6181 · HTML: https://mlsanity.com/paper/1408.6181/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Aug 2014.

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

- [AStitchInLanguageModels: Dataset and Methods for the Exploration of Idiomaticity in Pre-Trained Language Models](https://mlsanity.com/paper/2109.04413/) (2021)
- [JARVix at SemEval-2022 Task 2: It Takes One to Know One? Idiomaticity Detection using Zero and One-Shot Learning](https://mlsanity.com/paper/2202.02394/) (2022)
- [Contextualized word senses: from attention to compositionality](https://mlsanity.com/paper/2312.00680/) (2023)

## Abstract (arXiv)

This paper provides a method for improving tensor-based compositional distributional models of meaning by the addition of an explicit disambiguation step prior to composition. In contrast with previous research where this hypothesis has been successfully tested against relatively simple compositional models, in our work we use a robust model trained with linear regression. The results we get in two experiments show the superiority of the prior disambiguation method and suggest that the effectiveness of this approach is model-independent.