spacy-for-datashare

Let spaCy do the parsing of Named Entities for documents in the Datashare platform.

The idea: Datashare is a java-based platform that uses Apache Tika to extract text from documents. After text extraction, a Java-based NLP parser will execute a NER-task to find Named Entities. All documents and Named Enitites are stored in Datashare's Elasticsearch index. Instead of using the standard Java-based NLP-parsers, you can now use your own customized spaCy-models to parse Named Entities!

• spaCy is a free, open-source library for advanced Natural Language Processing (NLP) in Python
• Datashare allows you to better analyze information, in all its forms. It is a free open-source software platform for Mac/Windows/Linux developed by the International Consortium of Investigative Journalists

Prerequisites

• install Datashare
• upload documents to Datashare
• make your custom NER-filter visible in Datashare (for details, look here)
  • add your plugins-folder location, e.g. --pluginsDir "C:\Users\Name\AppData\Roaming\Datashare\plugins" (Windows) to "C:\program files\Datashare-${VERSION}\datashareStandalone.bat"
  • register a new filter via a index.js file in the plugins folder. For examples, see the plugins folder
• Use Python 3.8 or higher and install these python libraries
  • "tqdm>=4.0.0"
  • "spacy>=2.2.0"
  • "price_parser>=0.3.0"

Settings

# Your local model or a spacy default model like nl_core_news_sm
SPACY_MODEL = './data/spacy_model/nl-0.0.5/model-best' 

# Preprocess the line-end problems for PDF's extraced by TIKA (as good as possible)
PREPROCESS_TIKA_OUTPUT = True

# Skip already parsed documents
SKIP_ALREADY_PARSED_DOCS = False

# Prevent duplicate enitities after rerunning the script
CLEAN_ENTITIES_BEFORE_UPDATE = True

# Elastic search URL; e.g. for VMbox=10.0.2.2:9200 , for local installation=127.0.0.1:9200
ES_BASE_URL = 'http://10.0.2.2:9200/'

# Named Entity labels; depend on your spaCy model
ACCEPTED_SPACY_LABELS = ('PER', 'ORG', 'GPE', 'PER_C', 'ORG_C', 'NORP', 'LOC', 'EMAIL', 'URL', 'MONEY') 

Steps taken by script

• Get documents from Datashare's ElasticSearch index
• Preprocess raw content (mostly raw TIKA output)
• Parse doc with spaCy
• Delete all old Named Entities that are already in the ES-index
• Get all Named Entities and merge them in Datashare's format
• Bulk index the document updates and new Named Entities to ES-index
• Refresh ES-index