# Contextually Propagated Term Weights for Document Representation

arXiv: https://arxiv.org/abs/1906.00674 · HTML: https://mlsanity.com/paper/1906.00674/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jun 2019.

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

- [Coherence-Based Distributed Document Representation Learning for Scientific Documents](https://mlsanity.com/paper/2201.02846/) (2022)

## Abstract (arXiv)

Word embeddings predict a word from its neighbours by learning small, dense embedding vectors. In practice, this prediction corresponds to a semantic score given to the predicted word (or term weight). We present a novel model that, given a target word, redistributes part of that word's weight (that has been computed with word embeddings) across words occurring in similar contexts as the target word. Thus, our model aims to simulate how semantic meaning is shared by words occurring in similar contexts, which is incorporated into bag-of-words document representations. Experimental evaluation in an unsupervised setting against 8 state of the art baselines shows that our model yields the best micro and macro F1 scores across datasets of increasing difficulty.