Recent Releases of patcit
patcit - 0.3.0
🏷 v0.3.0
Data
- Major improvement of
bibliographical_referenceschema (harmonize grobid & crossref) for seamless analysis - Enrichment of
intext.patent - Add domain specific front page tables (
norm_standard,database,wiki)
Community
- Revisit BQ project architecture
- Add Colab notebooks integration
- Revisit
README.md
Code
- Lighter API
- Lighter dependencies
Models
- Add information extraction models
- Add models and training data DVC support
Validation
- Validation of in-text extraction models
Thanks
Special thanks to:
- Gabriele Cristelli (EPFL)
- Kyle Higham (Hitotsubashi University)
- Lucas Violon (HEC Paris)
- Jupyter Notebook
Published by cverluise over 5 years ago
patcit - v0.2-npl
🏷 v0.2-npl
The v0.2-npl introduces 2 major improvements:
- npl_class field. This field is predicted using a multi-class text classification model based on spaCy textCategorizer with the npl text as input. See focus and models binaries below.
- Propagate ISSN using title_j to bibliographical references with the same title_j but no match.
Focus on npl_class
en_core_web_sm_npl-class-ensemble-0.8
ensemblemodel (bow+cnn with bagging)- trained on 80% of the "gold" dataset and evaluated on remaining 20% (hold-out)
See in models/nplclasstraining/ for more
Average performance
accuracy|precision|recall|f1 ---|---|---|--- 0.9|0.89|0.88|0.88
Class performance
|precision|recall|f1|support ----|----|----|---- BIBLIOGRAPHICALREFERENCE|0.92|0.95|0.93|370.0 SEARCHREPORT|1.0|0.92|0.96|86.0 OFFICEACTION|0.99|0.93|0.96|76.0 DATABASE|0.89|0.73|0.8|11.0 WEBPAGE|0.53|0.53|0.53|17.0 PATENT|0.91|0.94|0.93|68.0 NA|1.0|1.0|1.0|6.0 PRODUCTDOCUMENTATION|0.44|0.43|0.44|37.0 NORM_STANDARD|0.86|0.6|0.71|20.0 LITIGATION|0.25|0.11|0.15|9.0
en_core_web_sm_npl-class-ensemble-1.0
Same as en_core_web_sm_npl-class-ensemble-1.0 but trained on full dataset to maximize performance. Model used to create the npl_class field.
- Jupyter Notebook
Published by cverluise over 6 years ago