# The Recent Advances in Automatic Term Extraction: A survey

arXiv: https://arxiv.org/abs/2301.06767 · HTML: https://mlsanity.com/paper/2301.06767/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 113 references that are in the map.
- First posted on arXiv 17 Jan 2023.

## Builds on (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Multilingual Denoising Pre-training for Neural Machine Translation](https://mlsanity.com/paper/2001.08210/) (2020)
- [Word Alignment by Fine-tuning Embeddings on Parallel Corpora](https://mlsanity.com/paper/2101.08231/) (2021)
- [Template-Based Named Entity Recognition Using BART](https://mlsanity.com/paper/2106.01760/) (2021)
- [Template-free Prompt Tuning for Few-shot NER](https://mlsanity.com/paper/2109.13532/) (2021)
- [Feature-Less End-to-End Nested Term Extraction](https://mlsanity.com/paper/1908.05426/) (2019)
- [KAS-term: Extracting Slovene Terms from Doctoral Theses via Supervised Machine Learning](https://mlsanity.com/paper/1906.02053/) (2019)
- [Ensembling Transformers for Cross-domain Automatic Term Extraction](https://mlsanity.com/paper/2212.05696/) (2022)

## Built on by (most cited first, 4 of 4)

- [An Improved Method for Class-specific Keyword Extraction: A Case Study in the German Business Registry](https://mlsanity.com/paper/2407.14085/) (2024)
- [CoastTerm: a Corpus for Multidisciplinary Term Extraction in Coastal Scientific Literature](https://mlsanity.com/paper/2406.09128/) (2024)
- [Crossing Domains without Labels: Distant Supervision for Term Extraction](https://mlsanity.com/paper/2510.06838/) (2025)
- [Enhancing Automatic Term Extraction with Large Language Models via Syntactic Retrieval](https://mlsanity.com/paper/2506.21222/) (2025)

## Abstract (arXiv)

Automatic term extraction (ATE) is a Natural Language Processing (NLP) task that eases the effort of manually identifying terms from domain-specific corpora by providing a list of candidate terms. As units of knowledge in a specific field of expertise, extracted terms are not only beneficial for several terminographical tasks, but also support and improve several complex downstream tasks, e.g., information retrieval, machine translation, topic detection, and sentiment analysis. ATE systems, along with annotated datasets, have been studied and developed widely for decades, but recently we observed a surge in novel neural systems for the task at hand. Despite a large amount of new research on ATE, systematic survey studies covering novel neural approaches are lacking. We present a comprehensive survey of deep learning-based approaches to ATE, with a focus on Transformer-based neural models. The study also offers a comparison between these systems and previous ATE approaches, which were based on feature engineering and non-neural supervised learning algorithms.