Intellias
Case study

NLP-Based Text Mining Platform

We developed a language-agnostic service that processes terabytes of text data

Key features

  • Analyze semantic of natural language

    Analyze semantic of natural language

  • Get relevant search results

    Get relevant search results

  • Classify information efficiently

    Classify information efficiently

Industry:
Social Media, Text Analytics
Market:
Austria
Team size:
9 employees
Cooperation:
2 years
Technologies:

AWS / Java / KNIME / REST API / Spring

About the client

An innovator in natural language processing and text mining solutions, our client develops semantic fingerprinting technology as the foundation for NLP text mining and artificial intelligence software. Our client’s company, based in Vienna and San Francisco, addresses the challenges of filtering large amounts of unstructured text data, detecting topics in real-time on social media, searching in multiple languages across millions of documents, natural language processing, and text mining. Our client was named a 2016 IDC Innovator in the machine learning-based text analytics market as well as one of the 100 startups using Artificial Intelligence to transform industries by CB Insights.

NLP-Based Text Mining Platform

Cortical came with the challenge

Inspired by the latest findings on how the human brain processes language, this Austria-based startup worked out a fundamentally new approach to mining large volumes of texts to create the first language-agnostic semantic engine. Fueled with hierarchical temporal memory (HTM) algorithms, this text mining software generates semantic fingerprints from any unstructured textual information, promising virtually unlimited use cases and a massive market opportunity.

Our client partnered with us to scale up their development team and bring to life their innovative semantic engine for text mining. Our expertise in REST, Spring, and Java was vital, as our client needed to develop a prototype that was capable of running complex meaning-based filtering, topic detection, and semantic search over huge volumes of unstructured text in real time.

Intellias developed the text mining solution

This language-agnostic service lets our client process terabytes of text data by encoding the semantics of natural language elements into semantically grounded binary code. The resulting code is then further compared and analyzed with standardized metrics, offering great opportunities for text filtering, classification, and search. This service is advantageous for businesses that need to search text repositories in various languages, monitor incoming emails, or detect current topics on social media.

We also presented a prototype of NLP algorithms integrated into KNIME workflows using Java snippet nodes. This is a configurable pipeline that takes unstructured scientific texts as inputs and returns structured data as the output. Users can specify preprocessing settings and analyses to be run on an arbitrary number of topics. The output can then be visualized graphically on the resulting similarity index.

What did our client get?

  • An NLP engine that operates on the level of semantics rather than keywords for filtering, classifying, clustering, and searching text
  • A horizontally scalable solution that’s ready to support exponential growth of the user base
  • A semantic fingerprinting technology to offer text data mining software as a service on the global market

We achieved great results together

We helped develop a product that produces semantic fingerprints of natural language processing platform. It’s a fundamentally new alternative to capturing the semantics of natural language. This product allows end clients to make intelligent decisions based on human-generated text inputs including words, documents, and social media streams.

We helped Cortical

  • Acquire new investors and raise $2 million in capital for their NLP and text mining platform
  • Implement RESTful APIs available on the Amazon Web Services marketplace to scale their solution with customized databases and applications
  • Monitor customers’ reactions in real time through an intelligent filter that converts the Twitter firehose into a stream of semantic fingerprints
  • Develop NLP algorithms in KNIME nodes to power a prototype of their NLP system

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