# A Review on Method Entities in the Academic Literature: Extraction, Evaluation, and Application

arXiv: https://arxiv.org/abs/2209.03687 · HTML: https://mlsanity.com/paper/2209.03687/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 139 references that are in the map.
- First posted on arXiv 8 Sep 2022.

## Builds on (most cited first, 17 of 17)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Energy and Policy Considerations for Deep Learning in NLP](https://mlsanity.com/paper/1906.02243/) (2019)
- [Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction](https://mlsanity.com/paper/1808.09602/) (2018)
- [SciREX: A Challenge Dataset for Document-Level Information Extraction](https://mlsanity.com/paper/2005.00512/) (2020)
- [SemEval 2017 Task 10: ScienceIE - Extracting Keyphrases and Relations from Scientific Publications](https://mlsanity.com/paper/1704.02853/) (2017)
- [Scientific Information Extraction with Semi-supervised Neural Tagging](https://mlsanity.com/paper/1708.06075/) (2017)
- [A Summarization System for Scientific Documents](https://mlsanity.com/paper/1908.11152/) (2019)
- [Investigating Software Usage in the Social Sciences: A Knowledge Graph Approach](https://mlsanity.com/paper/2003.10715/) (2020)
- [Information Extraction from Scientific Literature for Method Recommendation](https://mlsanity.com/paper/1901.00401/) (2018)
- [Using the Full-text Content of Academic Articles to Identify and Evaluate Algorithm Entities in the Domain of Natural Language Processing](https://mlsanity.com/paper/2010.10817/) (2020)
- [Entitymetrics: Measuring the Impact of Entities](https://mlsanity.com/paper/1309.2486/) (2013)
- [Handling Noisy Labels for Robustly Learning from Self-Training Data for Low-Resource Sequence Labeling](https://mlsanity.com/paper/1903.12008/) (2019)
- [Method and Dataset Entity Mining in Scientific Literature: A CNN + Bi-LSTM Model with Self-attention](https://mlsanity.com/paper/2010.13583/) (2020)
- [Semi-supervised Bootstrapping approach for Named Entity Recognition](https://mlsanity.com/paper/1511.06833/) (2015)
- [Challenges of measuring the impact of software: an examination of the lme4 R package](https://mlsanity.com/paper/1811.11270/) (2018)
- [The (re-)instrumentalization of the Diagnostic and Statistical Manual of Mental Disorders (DSM) in psychological publications: a citation context analysis](https://mlsanity.com/paper/2010.11101/) (2020)
- [AI Marker-based Large-scale AI Literature Mining](https://mlsanity.com/paper/2011.00518/) (2020)

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

- [Research Method Usage across Academic Ages in Library and Information Science: An Empirical Study (1990-2023)](https://mlsanity.com/paper/2606.21862/) (2026)
- [Exploring Academic Influence of Algorithms by Co-occurrence Network Based on Full-text of Academic Papers](https://mlsanity.com/paper/2606.24099/) (2026)
- [Data-Driven Evolution of Library and Information Science Research Methods (1990-2022): A Perspective Based on Fine-grained Method Entities](https://mlsanity.com/paper/2606.25320/) (2026)
- [Enhancing Scientific Named Entity Recognition via Large Language Models: A Type-driven Multi-task Learning Approach](https://mlsanity.com/paper/2608.08636/) (2026)

## Abstract (arXiv)

In scientific research, the method is an indispensable means to solve scientific problems and a critical research object. With the advancement of sciences, many scientific methods are being proposed, modified, and used in academic literature. The authors describe details of the method in the abstract and body text, and key entities in academic literature reflecting names of the method are called method entities. Exploring diverse method entities in a tremendous amount of academic literature helps scholars understand existing methods, select the appropriate method for research tasks, and propose new methods. Furthermore, the evolution of method entities can reveal the development of a discipline and facilitate knowledge discovery. Therefore, this article offers a systematic review of methodological and empirical works focusing on extracting method entities from full-text academic literature and efforts to build knowledge services using these extracted method entities. Definitions of key concepts involved in this review were first proposed. Based on these definitions, we systematically reviewed the approaches and indicators to extract and evaluate method entities, with a strong focus on the pros and cons of each approach. We also surveyed how extracted method entities are used to build new applications. Finally, limitations in existing works as well as potential next steps were discussed.