# Detecting Emerging Technologies and their Evolution using Deep Learning and Weak Signal Analysis

arXiv: https://arxiv.org/abs/2205.05449 · HTML: https://mlsanity.com/paper/2205.05449/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 80 references that are in the map.
- First posted on arXiv 11 May 2022.

## 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)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [Automatic Keyword Extraction for Text Summarization: A Survey](https://mlsanity.com/paper/1704.03242/) (2017)
- [Enhancing Keyphrase Extraction from Microblogs using Human Reading Time](https://mlsanity.com/paper/2010.09934/) (2020)
- [Enhancing Keyphrase Extraction from Academic Articles with their Reference Information](https://mlsanity.com/paper/2111.14106/) (2021)

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

- [Counterfactual Causal Inference in Natural Language with Large Language Models](https://mlsanity.com/paper/2410.06392/) (2024)
- [On the evolution of research in hypersonics: application of natural language processing and machine learning](https://mlsanity.com/paper/2208.08507/) (2022)
- [Detecting Emerging Technologies in Artificial Intelligence Scientific Ecosystem Using an Indicator-based Model](https://mlsanity.com/paper/2211.01348/) (2022)

## Abstract (arXiv)

Emerging technologies can have major economic impacts and affect strategic stability. Yet, early identification of emerging technologies remains challenging. In order to identify emerging technologies in a timely and reliable manner, a comprehensive examination of relevant scientific and technological (S&T) trends and their related references is required. This examination is generally done by domain experts and requires significant amounts of time and effort to gain insights. The use of domain experts to identify emerging technologies from S&T trends may limit the capacity to analyse large volumes of information and introduce subjectivity in the assessments. Decision support systems are required to provide accurate and reliable evidence-based indicators through constant and continuous monitoring of the environment and help identify signals of emerging technologies that could alter security and economic prosperity. For example, the research field of hypersonics has recently witnessed several advancements having profound technological, commercial, and national security implications. In this work, we present a multi-layer quantitative approach able to identify future signs from scientific publications on hypersonics by leveraging deep learning and weak signal analysis. The proposed framework can help strategic planners and domain experts better identify and monitor emerging technology trends.