Pattern-based Acquisition of Scientific Entities from Scholarly Article Titles
This work addresses the need for efficient metadata extraction in scholarly databases, but it is incremental as it applies an existing rule-based method to a specific domain.
The paper tackled the problem of automatically extracting scientific entities like research problems and methods from Computational Linguistics article titles using a rule-based approach, achieving an average precision of 75% on a dataset of 50,237 titles.
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%.