https://github.com/dhlab-epfl/LinkedBooksDeepReferenceParsing
A deep learning architecture for reference mining from literature in the arts and humanities.
https://github.com/dhlab-epfl/LinkedBooksDeepReferenceParsing
annotated-references annotations annotations-dataset citations crf crf-model dataset deep-learning footnotes venice
Last synced: 8 months ago
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A deep learning architecture for reference mining from literature in the arts and humanities.
- Host: GitHub
- URL: https://github.com/dhlab-epfl/LinkedBooksDeepReferenceParsing
- Owner: dhlab-epfl
- License: mit
- Created: 2018-02-01T21:33:24.000Z (over 8 years ago)
- Default Branch: master
- Last Pushed: 2019-08-14T09:56:26.000Z (almost 7 years ago)
- Last Synced: 2024-06-11T03:33:56.621Z (about 2 years ago)
- Topics: annotated-references, annotations, annotations-dataset, citations, crf, crf-model, dataset, deep-learning, footnotes, venice
- Language: Jupyter Notebook
- Homepage: https://www.frontiersin.org/articles/10.3389/frma.2018.00021/full
- Size: 5.45 MB
- Stars: 14
- Watchers: 7
- Forks: 4
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-scholarly-data-analysis - Citation Parsing in humanities
README
# Deep Reference Parsing
This repository contains the code for the following article:
@article{alves_deep_2018,
author = {{Rodrigues Alves, Danny and Giovanni Colavizza and Frédéric Kaplan}},
title = {{Deep Reference Mining from Scholarly Literature in the Arts and Humanities}},
journal = {{Frontiers in Research Metrics & Analytics}},
volume = 3,
number = 21,
year = 2018,
doi = {10.3389/frma.2018.00021}
}
## Task definition
We focus on the task of reference mining, instantiated into three tasks: reference components detection (task 1), reference typology detection (task 2) and reference span detection (task 3).
* Sequence: *G. Ostrogorsky, History of the Byzantine State, Rutgers University Press, 1986.*
* Task 1: *author author title title title title title publisher publisher publisher year*
* Task 2: *b-secondary i-secondary ... e-secondary*
* Task 3: *b-r i-r ... e-r*
## Contents
* `LICENSE` MIT.
* `README.md` this file.
* `dataset/`
* [train](dataset/clean_test.txt) Train split, CoNLL format.
* [test](dataset/clean_train.txt) Test split, CoNLL format.
* [validation](dataset/clean_valid.txt) Validation split, CoNLL format.
* [compressed dataset](dataset.tar.gz) Compressed dataset.
* [data facts](Data%20Facts.ipynb) a Python notebook to explore the dataset (number of references, tag distributions).
* [crf_baseline](crf_baseline) CRF baseline implementation details.
* [keras](keras) Keras implementation details.
* [tensorflow](tensorflow) TF implementation details.
## Dataset
Example of dataset entry (beginning of validation dataset, first line/sequence): Token Task1tag Task2tag Task3tag`:
-DOCSTART- -X- -X- o
C author b-secondary b-r
. author i-secondary i-r
Agnoletti author i-secondary i-r
, author i-secondary i-r
Treviso title i-secondary i-r
e title i-secondary i-r
le title i-secondary i-r
sue title i-secondary i-r
pievi title i-secondary i-r
. title i-secondary i-r
Illustrazione title i-secondary i-r
storica title i-secondary i-r
, title i-secondary i-r
Treviso publicationplace i-secondary i-r
1898 year i-secondary i-r
, year i-secondary i-r
2 publicationspecifications i-secondary i-r
v publicationspecifications e-secondary i-r
. publicationspecifications e-secondary e-r
Pre-trained word vectors can be downloaded from Zenodo: [](https://doi.org/10.5281/zenodo.1175213)
## Implementations
### CRF baseline
See internal [readme](crf_baseline/README.md) for details.
### Keras
See internal [readme](keras/README.md) for details.
### Tensor Flow
See internal [readme](tensorflow/README.md) for details.
This implementation borrows from [Guillaume Genthial's Sequence Tagging with Tensorflow](https://guillaumegenthial.github.io/sequence-tagging-with-tensorflow.html).