# Keyphrase Extraction Using Neighborhood Knowledge Based on Word Embeddings

arXiv: https://arxiv.org/abs/2111.07198 · HTML: https://mlsanity.com/paper/2111.07198/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 41 references that are in the map.
- First posted on arXiv 13 Nov 2021.

## Builds on (most cited first, 1 of 1)

- [Deep Keyphrase Generation](https://mlsanity.com/paper/1704.06879/) (2017)

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

- [From Statistical Methods to Deep Learning, Automatic Keyphrase Prediction: A Survey](https://mlsanity.com/paper/2305.02579/) (2023)
- [Pre-Trained Language Models for Keyphrase Prediction: A Review](https://mlsanity.com/paper/2409.01087/) (2024)

## Abstract (arXiv)

Keyphrase extraction is the task of finding several interesting phrases in a text document, which provide a list of the main topics within the document. Most existing graph-based models use co-occurrence links as cohesion indicators to model the relationship of syntactic elements. However, a word may have different forms of expression within the document, and may have several synonyms as well. Simply using co-occurrence information cannot capture this information. In this paper, we enhance the graph-based ranking model by leveraging word embeddings as background knowledge to add semantic information to the inter-word graph. Our approach is evaluated on established benchmark datasets and empirical results show that the word embedding neighborhood information improves the model performance.