Sintelix extracts entities, relationships and properties from any text. The built-in configuration identifies people, organisations, geo-tagged locations and 24 other entity types.

Sintelix text extraction


Multilingual entity extraction

Multilingual Entity Extraction

Sintelix has recently adopted advanced AI-based Named Entity Recognition (NER). We have achieved outstanding accuracy. Our new algorithms boast both low error rate and low miss rate. High accuracy is crucial for effective down-stream processing to gain reliable insights for decision-making. The languages we currently offer are:

      • English
      • Russian
      • Chinese
      • Spanish
      • Arabic
      • Farsi
      • Latvian
      • Thai


Sintelix does an excellent job of entity extraction and knowledge graph creation out of the box. Sintelix also offers all the tools you need to tune.

Sintelix offers three levels of tooling to extract information from documents. Each addresses a different level of complexity. We believe that simple problems should be solved with simple tools. Only if the problem demands it should more sophisticated tools be used.

  1. Dictionaries – most users can use dictionaries easily
  2. Entity Extraction Scripts – for lead users – very powerful
  3. Document Processing Scripts (JavaScript) – gives you total control
Configurable entity extraction
Edit your Markup with Three Editors

Edit your Markup with Three Editors

  • Page Editor – edit the markup or the text on the page directly – in edit mode
  • Search/Modify – Document Search’s modify capability to create and delete markup in bulk
  • Text Reference Refinement – review and edit text references across multiple collections

Network (Knowledge Graph) Creation

Network and Knowledge Graph Creation

The main purpose of Information Extraction is to give structure to the information contained in text. Sintelix works through the input data and recognises entities and the relationships between them. In Sintelix, we normally insert extracted information into a knowledge graph of entities and links (a network in Sintelix’s graph database). Entity Resolution takes entities extracted from documents and fuses them to create a unified network. For example, the network Person entity “Vladimir Putin” links to each of the documents that refer to him.

Sintelix networks can span vast collections of documents and expose the insights they contain in compact form.

You can view the networks and timelines Sintelix generates automatically immediately documents are ingested.


Global eye logo

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