SoftwareApplication
identifier: 10.5281/zenodo.8286578
name: nedextract
description: nedextract is a python library that helps you to extract information on person names and organisations mentioned in Dutch (annual report) PDF files.
description: ## Nedextract nedextract is developed to extract specific information from annual report PDF files that are written in Dutch. Currently it tries to do the following: - Read the PDF file, and perform Named Entity Recognition (NER) using Stanza to extract all persons and all organisations named in the document, which are then processed by the processes listed below. - Extract persons: using a rule-based method that searches for specific keywords, this module tries to identify: - Ambassadors - People in important positions in the organisation. The code tries to determine a main job description (e.g. director or board) and a sub-job description (e.g. chairman or treasurer). Note that these positions are identified and outputted in Dutch. The main jobs that are considered are: - directeur - raad van toezicht - bestuur - ledenraad - kascommissie - controlecommisie The sub positions that are considered are: - directeur - voorzitter - vicevoorzitter - lid - penningmeester - commissaris - adviseur For each person that is identified, the code searches for keywords in the sentences in which the name appears to determine the main position, or the sentence directly before or after that. Subjobs are determine based on words appearing directly before or after the name of a person for whom a main job has been determined. For the main jobs and sub positions, various ways of writing are considered in the keywords. Also before the search for the job-identification starts, name-deduplication is performed by creating lists of names that (likely) refer to one and the same person (e.g. Jane Doe and J. Doe). - Extract related organisations: - After Stanza NER collects all candidates for mentioned organisations, postprocessing tasks try to determine which of these candidates are most likely true candidates. This is done by considering: how often the terms is mentioned in the document, how often the term was identified as an organisation by Stanza NER, whether the term contains keywords that…
code repository: https://github.com/Transparency-in-the-non-profit-sector/nedextract
author: https://orcid.org/0000-0002-2800-8309
programming language: Jupyter Notebook
programming language: Python
programming language: Shell
license:
  creative work: creativeWork
    name: Apache-2.0

https://orcid.org/0000-0002-2800-8309
type: Person
given name: L.S.
family name: Ootes
name: L.S. Ootes
role name: Lead RSE
affiliation: Organization
  name: Netherlands eScience Center