# Feature-Less End-to-End Nested Term Extraction

arXiv: https://arxiv.org/abs/1908.05426 · HTML: https://mlsanity.com/paper/1908.05426/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Aug 2019.

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

- [The Recent Advances in Automatic Term Extraction: A survey](https://mlsanity.com/paper/2301.06767/) (2023)
- [Ensembling Transformers for Cross-domain Automatic Term Extraction](https://mlsanity.com/paper/2212.05696/) (2022)
- [Vocab-Expander: A System for Creating Domain-Specific Vocabularies Based on Word Embeddings](https://mlsanity.com/paper/2308.03519/) (2023)
- [Extracting domain-specific terms using contextual word embeddings](https://mlsanity.com/paper/2502.17278/) (2025)

## Abstract (arXiv)

In this paper, we proposed a deep learning-based end-to-end method on the domain specified automatic term extraction (ATE), it considers possible term spans within a fixed length in the sentence and predicts them whether they can be conceptual terms. In comparison with current ATE methods, the model supports nested term extraction and does not crucially need extra (extracted) features. Results show that it can achieve high recall and a comparable precision on term extraction task with inputting segmented raw text.