# Pattern-based Acquisition of Scientific Entities from Scholarly Article Titles

arXiv: https://arxiv.org/abs/2109.00199 · HTML: https://mlsanity.com/paper/2109.00199/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 37 references that are in the map.
- First posted on arXiv 1 Sep 2021.

## Builds on (most cited first, 11 of 11)

- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction](https://mlsanity.com/paper/1808.09602/) (2018)
- [Data Programming: Creating Large Training Sets, Quickly](https://mlsanity.com/paper/1605.07723/) (2016)
- [Construction of the Literature Graph in Semantic Scholar](https://mlsanity.com/paper/1805.02262/) (2018)
- [SemEval 2017 Task 10: ScienceIE - Extracting Keyphrases and Relations from Scientific Publications](https://mlsanity.com/paper/1704.02853/) (2017)
- [Identification of Tasks, Datasets, Evaluation Metrics, and Numeric Scores for Scientific Leaderboards Construction](https://mlsanity.com/paper/1906.09317/) (2019)
- [Scientific Information Extraction with Semi-supervised Neural Tagging](https://mlsanity.com/paper/1708.06075/) (2017)
- [Domain-independent Extraction of Scientific Concepts from Research Articles](https://mlsanity.com/paper/2001.03067/) (2020)
- [The STEM-ECR Dataset: Grounding Scientific Entity References in STEM Scholarly Content to Authoritative Encyclopedic and Lexicographic Sources](https://mlsanity.com/paper/2003.01006/) (2020)
- [Method and Dataset Mining in Scientific Papers](https://mlsanity.com/paper/1911.13096/) (2019)
- [AppTechMiner: Mining Applications and Techniques from Scientific Articles](https://mlsanity.com/paper/1709.03064/) (2017)

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

- [Computer Science Named Entity Recognition in the Open Research Knowledge Graph](https://mlsanity.com/paper/2203.14579/) (2022)
- [Clustering Semantic Predicates in the Open Research Knowledge Graph](https://mlsanity.com/paper/2210.02034/) (2022)
- [Overview of STEM Science as Process, Method, Material, and Data Named Entities](https://mlsanity.com/paper/2205.11863/) (2022)
- [Instruction Finetuning for Leaderboard Generation from Empirical AI Research](https://mlsanity.com/paper/2408.10141/) (2024)

## Abstract (arXiv)

We describe a rule-based approach for the automatic acquisition of salient scientific entities from Computational Linguistics (CL) scholarly article titles. Two observations motivated the approach: (i) noting salient aspects of an article's contribution in its title; and (ii) pattern regularities capturing the salient terms that could be expressed in a set of rules. Only those lexico-syntactic patterns were selected that were easily recognizable, occurred frequently, and positionally indicated a scientific entity type. The rules were developed on a collection of 50,237 CL titles covering all articles in the ACL Anthology. In total, 19,799 research problems, 18,111 solutions, 20,033 resources, 1,059 languages, 6,878 tools, and 21,687 methods were extracted at an average precision of 75%.