Awesome
DptOIE and Models
DptOIE method uses the Dependence Parser and Part of Speech Tagger models trained with Stanford CoreNLP.
This project contains:
- DptOIE's source code: An Open Information Extraction for Portuguese language.
- Dependence Parser and Part of Speech Tagger models trained with Stanford CoreNLP.
Prerequisites to run from source code
- Dataset CETEN200, WIKI200 and models
- Insert the file pt-dep-parser.gz in pt-models directory
- Import as Maven Project in Eclipse IDE
How to use
To run the DptOIE.jar
java -jar DptOIE.jar -sentencesIN **sentences_file_path**
To use the module that handles subordinate clause
-SC true
To use the module that handles coordinated conjunctions
-CC true
To use the module that handles appositive
-appositive 1
To apply transitivity
-appositive 2
DptOIE is independent of dependency parser, so it can receive annotated sentences with other dependency parsers, as long as they are in ConLL-U format with the same tagsets of the Google treebank Treebanks Universal V2,1.
To run DptOIE from a dependency tree in ConLL-U format
java -jar DptOIE.jar -sentencesIN 'sentences_file_path' -dependencyTreeIN 'dependency_Tree_conllu_format'
Contributing
Please use the GitHub issue tracker
Authors
- Leandro Souza de Oliveira
- Daniela Barreiro Claro
How to cite
If you find this repo helpful, please consider citing:
@article{DBLP:journals/air/OliveiraCS23,
author = {Leandro Oliveira and
Daniela Barreiro Claro and
Marlo Souza},
title = {DptOIE: a Portuguese open information extraction based on dependency
analysis},
journal = {Artif. Intell. Rev.},
volume = {56},
number = {7},
pages = {7015--7046},
year = {2023},
url = {https://doi.org/10.1007/s10462-022-10349-4},
doi = {10.1007/s10462-022-10349-4},
timestamp = {Thu, 15 Jun 2023 21:57:36 +0200},
biburl = {https://dblp.org/rec/journals/air/OliveiraCS23.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
- Leandro Oliveira, Daniela Barreiro Claro, Marlo Souza: DptOIE: a Portuguese open information extraction based on dependency analysis. Artif. Intell. Rev. 56(7): 7015-7046 (2023).