CL-TitleParser parses and types scientific entities from the titles of Computational Linguistics scholarly articles written in English.
Specifically, types the entities as one of six concepts: research problem, solution, resource, language, tool, and method.
It is developed as part of the Open Research Knowledge Graph Project at TIB.
The code released in this repository is the standalone version of the parser.
ORKGTitleParser features a native Python implementation requiring minimal effort to set up. Please see usage instructions below.
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Requirements
- Python (3.7 or higher)
Clone this repository locally and run the parser as follows:
python parse_titles.py <input_file> <output_data_dir>
where input_file is a file with the papers' titles to be parsed with a new title in each line and output_data_dir is a user-specified local directory where the parsed output from the program will be written.
Additional information on running the parser can be found here
If you use the title parser in your work, please cite our ICADL 2021 Short Paper. Article preprint available.
@InProceedings{10.1007/978-3-030-91669-5_31,
author="D'Souza, Jennifer
and Auer, S{\"o}ren",
editor="Ke, Hao-Ren
and Lee, Chei Sian
and Sugiyama, Kazunari",
title="Pattern-Based Acquisition of Scientific Entities from Scholarly Article Titles",
booktitle="Towards Open and Trustworthy Digital Societies",
year="2021",
publisher="Springer International Publishing",
address="Cham",
pages="401--410",
isbn="978-3-030-91669-5"
}
An updated version of the parser that extracts seven concepts from short titles is available here.