# Document-Level Definition Detection in Scholarly Documents: Existing Models, Error Analyses, and Future Directions

arXiv: https://arxiv.org/abs/2010.05129 · HTML: https://mlsanity.com/paper/2010.05129/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Oct 2020.

## Built on by (most cited first, 11 of 11)

- [NaturalProofs: Mathematical Theorem Proving in Natural Language](https://mlsanity.com/paper/2104.01112/) (2021)
- [Lean-STaR: Learning to Interleave Thinking and Proving](https://mlsanity.com/paper/2407.10040/) (2024)
- [Understanding Jargon: Combining Extraction and Generation for Definition Modeling](https://mlsanity.com/paper/2111.07267/) (2021)
- [ACCoRD: A Multi-Document Approach to Generating Diverse Descriptions of Scientific Concepts](https://mlsanity.com/paper/2205.06982/) (2022)
- [The Semantic Reader Project: Augmenting Scholarly Documents through AI-Powered Interactive Reading Interfaces](https://mlsanity.com/paper/2303.14334/) (2023)
- [From Hypothesis to Publication: A Comprehensive Survey of AI-Driven Research Support Systems](https://mlsanity.com/paper/2503.01424/) (2025)
- [AT-BERT: Adversarial Training BERT for Acronym Identification Winning Solution for SDU@AAAI-21](https://mlsanity.com/paper/2101.03700/) (2021)
- [Generating Topic Pages for Scientific Concepts Using Scientific Publications](https://mlsanity.com/paper/2304.11922/) (2023)
- [Transferring Natural Language Datasets Between Languages Using Large Language Models for Modern Decision Support and Sci-Tech Analytical Systems](https://mlsanity.com/paper/2410.14074/) (2024)
- [Complex Mathematical Symbol Definition Structures: A Dataset and Model for Coordination Resolution in Definition Extraction](https://mlsanity.com/paper/2305.14660/) (2023)
- [Extracting Definienda in Mathematical Scholarly Articles with Transformers](https://mlsanity.com/paper/2311.12448/) (2023)

## Abstract (arXiv)

The task of definition detection is important for scholarly papers, because papers often make use of technical terminology that may be unfamiliar to readers. Despite prior work on definition detection, current approaches are far from being accurate enough to use in real-world applications. In this paper, we first perform in-depth error analysis of the current best performing definition detection system and discover major causes of errors. Based on this analysis, we develop a new definition detection system, HEDDEx, that utilizes syntactic features, transformer encoders, and heuristic filters, and evaluate it on a standard sentence-level benchmark. Because current benchmarks evaluate randomly sampled sentences, we propose an alternative evaluation that assesses every sentence within a document. This allows for evaluating recall in addition to precision. HEDDEx outperforms the leading system on both the sentence-level and the document-level tasks, by 12.7 F1 points and 14.4 F1 points, respectively. We note that performance on the high-recall document-level task is much lower than in the standard evaluation approach, due to the necessity of incorporation of document structure as features. We discuss remaining challenges in document-level definition detection, ideas for improvements, and potential issues for the development of reading aid applications.