https://github.com/centrefordigitalhumanities/textminer

A script to detect named entities and store them in an Elasticsearch annotated_text field
https://github.com/centrefordigitalhumanities/textminer

annotation elasticsearch ner spacy

Last synced: 10 days ago
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A script to detect named entities and store them in an Elasticsearch annotated_text field

• Host: GitHub
• URL: https://github.com/centrefordigitalhumanities/textminer
• Owner: CentreForDigitalHumanities
• License: mit
• Created: 2023-11-01T13:39:48.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2024-02-29T11:41:05.000Z (9 months ago)
• Last Synced: 2024-04-24T11:12:06.868Z (7 months ago)
• Topics: annotation, elasticsearch, ner, spacy
• Language: Python
• Homepage:
• Size: 17.6 KB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 4
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# TextMiNER

TextMiNER is a collection of scripts to perform named entity recognition (NER) in text, using the Python library [spaCy](https://spacy.io/). The detected named entities are saved in an Elasticsearch [annotated-text](https://www.elastic.co/guide/en/elasticsearch/plugins/8.10/mapper-annotated-text.html) field.

## Requirements

- Python 3.10 or newer

- Elasticsearch 8 or newer

- Elasticsearch's annotated-field plugin. To install, run:

```

sudo bin/elasticsearch-plugin install mapper-annotated-text

```

## Docker

This repository contains Docker images and a `docker-compose` file for runnig and testing the scripts locally. `docker-compose` requires an `.env` file, to be created next to `docker-compose.yaml`, with the following values:

```

ES_HOST=elasticsearch

ELASTIC_ROOT_PASSWORD={password-of-your-choice}

```

## Usage

### Environment

Before running the script, define your environment variables to set correct values for `ES_HOST` if you don't run Elasticsearch on localhost, and `API_ID`, `API_KEY` and `CERTS_LOCATION`, if you access an Elasticsearch cluster using an API key.

### SpaCy models

Make sure you have the required SpaCy models for NER analysis by running

```

python -m spacy download en_core_web_sm

python -m spacy download nl_core_news_sm

```

### Run the script (without Docker)

To analyze data from an Elasticsearch index with SpaCy, and save this data back into an annotated field, change to the `code` directory (`cd code`) and then run the following command:

`python process_documents.py -i {index_name} -f {field_name} -l {language}`

To run this for an English language corpus indexed as "test", which has text data saved in field "content", you could run

`python process_documents.py -i test -f content -l english`

### Run the script locally (with Docker)

Altenatively, running with Docker, without changing to `code` first, run

`docker-compose run --rm backend python process_documents.py -i {index_name} -f {field_name} -l {language}`