# Evaluating the Utility of Document Embedding Vector Difference for Relation Learning

arXiv: https://arxiv.org/abs/1907.08184 · HTML: https://mlsanity.com/paper/1907.08184/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jul 2019.

## Built on by (most cited first, 1 of 1)

- [We've had this conversation before: A Novel Approach to Measuring Dialog Similarity](https://mlsanity.com/paper/2110.05780/) (2021)

## Abstract (arXiv)

Recent work has demonstrated that vector offsets obtained by subtracting pretrained word embedding vectors can be used to predict lexical relations with surprising accuracy. Inspired by this finding, in this paper, we extend the idea to the document level, in generating document-level embeddings, calculating the distance between them, and using a linear classifier to classify the relation between the documents. In the context of duplicate detection and dialogue act tagging tasks, we show that document-level difference vectors have utility in assessing document-level similarity, but perform less well in multi-relational classification.