# Mapping Researcher Activity based on Publication Data by means of Transformers

arXiv: https://arxiv.org/abs/2306.09049 · HTML: https://mlsanity.com/paper/2306.09049/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 10 references that are in the map.
- First posted on arXiv 15 Jun 2023.

## Builds on (most cited first, 5 of 5)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [Evolution of Semantic Similarity -- A Survey](https://mlsanity.com/paper/2004.13820/) (2020)
- [Aspect-based Document Similarity for Research Papers](https://mlsanity.com/paper/2010.06395/) (2020)

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

- [Contextual Categorization Enhancement through LLMs Latent-Space](https://mlsanity.com/paper/2404.16442/) (2024)

## Abstract (arXiv)

Modern performance on several natural language processing (NLP) tasks has been enhanced thanks to the Transformer-based pre-trained language model BERT. We employ this concept to investigate a local publication database. Research papers are encoded and clustered to form a landscape view of the scientific topics, in which research is active. Authors working on similar topics can be identified by calculating the similarity between their papers. Based on this, we define a similarity metric between authors. Additionally we introduce the concept of self-similarity to indicate the topical variety of authors.