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A deep learning architecture for reference mining from literature in the arts and humanities.
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Data Facts.ipynb

Deep Reference Parsing

This repository contains the code for the following article:

      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


  • this file.
  • dataset/
    • train Train split, CoNLL format.
    • test Test split, CoNLL format.
    • validation Validation split, CoNLL format.
  • compressed dataset Compressed dataset.
  • data facts a Python notebook to explore the dataset (number of references, tag distributions).
  • crf_baseline CRF baseline implementation details.
  • keras Keras implementation details.
  • tensorflow TF implementation details.


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: DOI


CRF baseline

See internal readme for details.


See internal readme for details.

Tensor Flow

See internal readme for details.

This implementation borrows from Guillaume Genthial's Sequence Tagging with Tensorflow.

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