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Enrique Noriega edited this page Jan 27, 2021
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- Java 8 or Java 11
-
sbt
(any version will do, as the proper version will be retrieved at compile time) - At least 5G of RAM (see the
.sbtopts
file)
Reach is available under a dual license (free for research, not-so-free for commercial use).
You can find a description our supported input formats here: https://github.com/clulab/reach/wiki/Supported-Input-Formats
Here are two solutions:
- Use a url of this format:
http://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=pubmed&id=<pmc id sans pmc goes here>&retmode=xml
- If we wanted to retrieve
PMC26816343
, this would be the formatted url:http://eutils.ncbi.nlm.nih.gov/entrez/eutils/efetch.fcgi?db=pubmed&id=26816343&retmode=xml
- You can run this
Python
(2.7 or 3.x) script: https://gist.github.com/myedibleenso/f233359445461a71ad37017393fe921f
If you use Reach, please cite this paper:
@inproceedings{Valenzuela+:2015aa,
author = {Valenzuela-Esc\'{a}rcega, Marco A. and Gustave Hahn-Powell and Thomas Hicks and Mihai Surdeanu},
title = {A Domain-independent Rule-based Framework for Event Extraction},
organization = {ACL-IJCNLP 2015},
booktitle = {Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing: Software Demonstrations (ACL-IJCNLP)},
url = {http://www.aclweb.org/anthology/P/P15/P15-4022.pdf},
year = {2015},
pages = {127--132},
Note = {Paper available at \url{http://www.aclweb.org/anthology/P/P15/P15-4022.pdf}},
}
If your work makes use of the coreference resolution system (used by default in Reach), please cite this paper:
@InProceedings{Bell:16,
title = {{Sieve-based coreference resolution in the biomedical domain}},
author = {{Bell, Dane and Gus Hahn-Powell and Marco A. Valenzuela-Esc\'{a}rcega and Mihai Surdeanu}},
booktitle = {Proceedings of the 10th edition of the Language Resources and Evaluation Conference (LREC)},
year = {2016},
}
While Reach makes use of machine learning for aspects of ner, context, and causal assembly, event detection is done using rules written in Odin.