Skip to content
MinScIE is an Open Information Extraction system which provides structured knowledge enriched with semantic information about citations.
Java Python
Branch: MinScIE
Clone or download
Pull request Compare This branch is 8 commits ahead of YideSong:MinScIE.
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
SVM_model
src/main
.gitignore
CitationSentences.csv
LICENSE.txt
OriginalMinIE.csv
README.md
pom.xml

README.md

MinScIE: Citation-centered Open Information Extraction

An Open Information Extraction (OIE) system which provides structured knowledge enriched with semantic information about citations. This system is based upon the OIE system MinIE.

Open Information Extraction (OIE)

Open Information Extraction (OIE) systems aim to extract unseen relations and their arguments from unstructured text in unsupervised manner. In its simplest form, given a natural language sentence, they extract information in the form of a triple, consisted of subject (S), relation (R) and object (O).

Suppose we have the following input sentence:

AMD, which is based in U.S., is a technology company.

An OIE system aims to make the following extractions:

("AMD"; "is based in"; "U.S.")
("AMD"; "is"; "technology company")

Demo

For the demos, please refer to the classes tests.minie.Demo.java and tests.minie.DetectCitationDemo.java.

Citing

If you use MinScIE in your work, please cite our paper:

@inproceedings{lauscher2019minscie,
  title={MinScIE: Citation-centered Open Information Extraction},
  author={Lauscher, Anne and Song, Yide and Gashteovski, Kiril},
  booktitle={Proceedings of ACM/IEEE Joint Conference on Digital Libraries},
  year={2019}
}
You can’t perform that action at this time.