Named Entity Recognition API used by TEI Publisher

Related tags

Text Data & NLP tei-publisher-ner
Overview

TEI Publisher Named Entity Recognition API

This repository contains the API used by TEI Publisher's web-annotation editor to detect entities in the input text.

Named entity recognition is based on spaCy and python. Within TEI Publisher the services communicate as follows:

  1. TEI Publisher extracts the plain text of a TEI document, remembering the original position of each text fragment within the TEI XML
  2. The plain text is sent to the /entities endpoint of the named entity recognition API, which returns a JSON array of the entities found
  3. TEI Publisher re-maps each received entity back to its position in the original TEI XML and creates an annotation, which is inserted into the web annotation editor

Installation

  1. Install dependencies by running

    pip3 install -r requirements.txt

  2. Download one or more trained spaCy pipelines, e.g. for German:

    python -m spacy download en_core_web_sm

  3. Start the service with

    uvicorn main:app --reload --port 8001

8001 is the default port configured in TEI Publisher.

API Documentation

You can view the API documentation here: http://localhost:8001/docs

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Comments
  • SpaCy model for the Polish language

    SpaCy model for the Polish language

    In the current version to use languages other than German and English, I can add an additional download in Dockerfile (although I have already installed model pl_core_news_md on a working container and it also works after a reboot).

    But there will be a problem, because the standard 'Polish' spaCy models use slightly different NER labels. E.g. 'persName', 'orgName'. Would it be possible to consider this in the standard main.py file and expand dict MAPPINGS?

    MAPPINGS = { "person": ("PER", "PERSON", "persName"), "place": ("LOC", "GPE", "placeName", "geogName"), "organization": ("ORG", "orgName"), "author": ("AUT") }

    Looking through the documentation of the models on the spaCy website it seems like most use standard labels like PER, PERSON. The other exception is the Swedish model where there is a PRS (=Person as I'm guessing [just found a description here https://svn.spraakdata.gu.se/sb-arkiv/pub/swener-tags.html]) label. What I haven't tried yet is training the model, I don't know if these changes are enough.

    opened by pjaskulski 0
Owner
e-editiones.org
e-editiones is an international society to promote open standards and free software for digital editions with a focus on TEI-Publisher
e-editiones.org
GitHub
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