Text Mining Engine


Text mining engine TMS

The Text Mining Engine (TMS) performs semantic based retrieval, information reorganization, and multi-dimensional analysis by grasping the characteristics, meanings, and associations of large-scale internal and external non-structured data. It provides a variety of non-structured data analysis functions to discover and add value to hidden knowledge, so that good decisions such as high knowledge utilization, customer management, risk management, and research and development can be derived. It provides the functions to extract high quality information from vast amount of documents and information, extract relationships, categorize automatic information (documents), cluster automatic information (documents), summarize automatic information (documents), and analyze intelligent unstructured data. It is an intelligent text mining engine that dramatically reduces the time required for searching, analyzing and utilizing knowledge information.


<Text mining engine block diagram>

Main Features

  • Built-in powerful text mining feature
  • Built-in machine learning and deep learning-based high-quality morpheme analyzer
  • Built-in machine learning and deep learning-based high-performance sentence structure analyzer
  • Built-in machine learning and deep learning-based object name recognizer
  • Built-in machine learning and deep learning-based reputation (emotion) extractor
  • Built-in high-quality hybrid-type information extractor
  • Best existing built-in machine learning-based big data automatic information (document) classifier
  • Automatic built-in information (document) assembler

Main features and specifications

Natural language processing features

All high-precision language analyzers use machine learning and artificial neural network technology and can optimize the quality for each domain through dictionaries and rules.


<natural language processing features>

Information (document) automatic categorization feature

It has the capability to classify real-time hierarchies by a pre-defined classification system (category) for large amounts of non-structured big data (information and documents). So it’s a hybrid classification feature in which document classification and learning and rule-based can be combined.

  • Automatic information (document) cluster analysis feature
  • Knowledge trends analysis feature
  • Related words analysis feature – topic rank
  • Topic trends analysis feature
  • Rapidly rising keyword analysis feature – TopN
  • Spatial analysis feature

Main engine screen