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Python package to convert spaCy and Stanza documents to NLP Annotation Format (NAF)

Project description

nafigator

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DISCLAIMER - BETA PHASE

This parser to naf is currently in a beta phase.

Python package to convert text documents to NLP Annotation Format (NAF)

Features

Nafigator allows you to store NLP output from custom made spaCy and stanza pipelines with (intermediate) results and all processing steps in one format.

  • Convert text files to .naf files that satisfy the NLP Annotation Format (NAF)

    • Supported input media types: application/pdf (.pdf), text/plain (.txt)

    • Supported output format: .naf (xml)

    • Supported NLP pipelines: spaCy, stanza

    • Supported NAF layers: raw, text, terms, entities, deps

  • Read .naf documents and access data as Python lists and dicts

The NAF format

Key features:

  • Multilayered extensible annotations;

  • Reproducible NLP pipelines;

  • NLP processor agnostic;

  • Compatible with RDF

References:

Installation

To install the package

pip install nafigator

To install the package from Github

pip install -e git+https://github.com/wjwillemse/nafigator.git#egg=nafigator

How to run

Command line interface

To parse an pdf or a txt file run in the root of the project:

python -m nafigator.parse

Function calls

Example:

from nafigator.parse import generate_naf

doc = generate_naf(input = "../data/example.pdf",
                   engine = "stanza",
                   language = "en",
                   naf_version = "v3.1",
                   dtd_validation = False,
                   params = {'fileDesc': {'author': 'W.J.Willemse'}},
                   nlp = None)
  • input: text document to convert to naf document

  • engine: pipeline processor, i.e. ‘spacy’ or ‘stanza’

  • language: ‘en’ or ‘nl’

  • naf_version: ‘v3’ or ‘v3.1’

  • dtd_validation: True or False (default = False)

  • params: dictionary with parameters (default = {})

  • nlp: custom made pipeline object from spacy or stanza (default = None)

Get the document and processors metadata via:

doc.header

Output of doc.header of processed data/example.pdf:

{
        'fileDesc': {
                'author': 'W.J.Willemse',
                'creationtime': '2021-04-25T11:28:58UTC',
                'filename': 'data/example.pdf',
                'filetype': 'application/pdf',
                'pages': '2'},
        'public': {
                '{http://purl.org/dc/elements/1.1/}uri': 'data/example.pdf',
                '{http://purl.org/dc/elements/1.1/}format': 'application/pdf'},
                ...

Get the raw layer output via:

doc.raw

Output of doc.raw of processed data/example.pdf:

The cat sat on the mat. Matt was his name.

Get the text layer output via:

doc.text

Output of doc.text of processed data/example.pdf:

[
        {'text': 'The', 'page': '1', 'sent': '1', 'id': 'w1', 'length': '3', 'offset': '0'},
        {'text': 'cat', 'page': '1', 'sent': '1', 'id': 'w2', 'length': '3', 'offset': '4'},
        {'text': 'sat', 'page': '1', 'sent': '1', 'id': 'w3', 'length': '3', 'offset': '8'},
        {'text': 'on', 'page': '1', 'sent': '1', 'id': 'w4', 'length': '2', 'offset': '12'},
        {'text': 'the', 'page': '1', 'sent': '1', 'id': 'w5', 'length': '3', 'offset': '15'},
        {'text': 'mat', 'page': '1', 'sent': '1', 'id': 'w6', 'length': '3', 'offset': '19'},
        {'text': '.', 'page': '1', 'sent': '1', 'id': 'w7', 'length': '1', 'offset': '22'},
        {'text': 'Matt', 'page': '1', 'sent': '2', 'id': 'w8', 'length': '4', 'offset': '24'},
        {'text': 'was', 'page': '1', 'sent': '2', 'id': 'w9', 'length': '3', 'offset': '29'},
        {'text': 'his', 'page': '1', 'sent': '2', 'id': 'w10', 'length': '3', 'offset': '33'},
        {'text': 'name', 'page': '1', 'sent': '2', 'id': 'w11', 'length': '4', 'offset': '37'},
        {'text': '.', 'page': '1', 'sent': '2', 'id': 'w12', 'length': '1', 'offset': '41'}
]

Get the terms layer output via:

doc.terms

Output of doc.terms of processed data/example.pdf:

[
        {'id': 't1', 'lemma': 'the', 'pos': 'DET', 'targets': ['w1']},
        {'id': 't2', 'lemma': 'cat', 'pos': 'NOUN', 'targets': ['w2']},
        {'id': 't3', 'lemma': 'sit', 'pos': 'VERB', 'targets': ['w3']},
        {'id': 't4', 'lemma': 'on', 'pos': 'ADP', 'targets': ['w4']},
        {'id': 't5', 'lemma': 'the', 'pos': 'DET', 'targets': ['w5']},
        {'id': 't6', 'lemma': 'mat', 'pos': 'NOUN', 'targets': ['w6']},
        {'id': 't7', 'lemma': '.', 'pos': 'PUNCT', 'targets': ['w7']},
        {'id': 't8', 'lemma': 'Matt', 'pos': 'PROPN', 'targets': ['w8']},
        {'id': 't9', 'lemma': 'be', 'pos': 'AUX', 'targets': ['w9']},
        {'id': 't10', 'lemma': 'he', 'pos': 'PRON', 'targets': ['w10']},
        {'id': 't11', 'lemma': 'name', 'pos': 'NOUN', 'targets': ['w11']},
        {'id': 't12', 'lemma': '.', 'pos': 'PUNCT', 'targets': ['w12']}]

Get the entities layer output via:

doc.entities

Output of doc.entities of processed data/example.pdf:

[
        {'id': 'e1', 'type': 'PERSON', 'targets': ['t8']}
]

Get the entities layer output via:

doc.deps

Output of doc.deps of processed data/example.pdf:

[
        {'from': 't2', 'to': 't1', 'rfunc': 'det'},
        {'from': 't3', 'to': 't2', 'rfunc': 'nsubj'},
        {'from': 't6', 'to': 't4', 'rfunc': 'case'},
        {'from': 't3', 'to': 't6', 'rfunc': 'obl'},
        {'from': 't6', 'to': 't5', 'rfunc': 'det'},
        {'from': 't3', 'to': 't7', 'rfunc': 'punct'},
        {'from': 't11', 'to': 't8', 'rfunc': 'nsubj'},
        {'from': 't11', 'to': 't9', 'rfunc': 'cop'},
        {'from': 't11', 'to': 't10', 'rfunc': 'nmod:poss'},
        {'from': 't11', 'to': 't12', 'rfunc': 'punct'}
]

Get the formats layer output via:

doc.formats

Output of doc.formats:

[
        {'length': '45', 'offset': '0', 'textboxes': [
                {'textlines': [
                        {'texts': [
                                {'font': 'CIDFont+F1',
                                 'size': '12.000',
                                 'length': '42',
                                 'offset': '0',
                                 'text': 'The cat sat on the mat. Matt was his name.'}]
                        }
                }]
        ]}
]

Credits

This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.

History

0.1.0 (2021-03-13)

  • First release on PyPI.

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