Text Analytics, Information Retrieval and NLP Functionality for the world’s leading contract & document management firm

Enabled a document management firm to mine data accurately and improve operations efficiency through automation

Intelligent-Doc-Miner-with-OCR-and-ML

About the Client

The client is a world-leading contract and document management firm based in the US. The client
handles large volumes of contracts and documents for leading companies around the globe. The client helps companies to retain and grow their base revenue with effective document management.

The Business Challenge

The client wanted to apply text mining, information retrieval, and NLP (Natural Language Processing) on the documents that they manage which is more than 100,000. The client wanted to derive metadata information to automate their process thereby eliminating revenue leaks and anomalies for their clients.

What Aptus Data Labs Did

We built an AI-based LDA (Latent Dirichlet Allocation) model to mine metadata efficiently.

The Impact Aptus Data Labs Made

The new text analytics LDA model successfully extracted useful metadata to increase accuracy by 85% and operations efficiency by 35%.

The Business and Technology Approach

Aptus Data Labs used the below process to build the text analytics model to resolve the existing
challenge. Aptus Data Labs:

  • Used text filtration to extract data based on titles, dates, contract party details, document category, and contact details
  • Used the following application to retrieve useful data
  • Entity Extraction
  • Entity Extraction
  • Name Matching
  • Stop Word Removal
  • Stemming
  • Tokenization
  • Categorization
  • Topic Extraction
  • Transformation of Cases
  • Words to Vector
  • Sentence Extraction
  • Naïve Bayes Classification

Work Flow

Available on Request

Tools used

Rapid Miner
‘R’ Code integrated into Rapid Miner

The Outcome

                • The AI-based text analytics model successfully generated the metadata required by the client. The model was able to extract information with 85% accuracy. The derived data helped the client to automate their process and increased their operations efficiency by 35%.

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