download-pdfMARKET NEED

Many companies face an increase in data volume of unstructured text documents from customer feedback (surveys, customer support, chatlines, correspondence) or from company-specific documentation.

There is a need to quickly understand the content of such data and its main topics. User-tagging of large volume of text data entries is expensive, time-consuming and user-dependent.

A solution is to automatically assign tags to such datasets. Tags can be extracted directly from the text data or can represent text content at a semantic level.

Tags can have multiple uses:

  • Re-structuring data collections to support classification of data;
  • Information retrieval based on new and more relevant attributes;
  • Finding documents linked through common tags.

TagTell visualization illustrating most frequent tags extracted from a data set of financial news articles

TECHNOLOGY SOLUTION

TagTell is an interactive web application based on unsupervised key-phrase extraction methods and Natural Language Processing.

Three domain-independent, unsupervised approaches are implemented:

  • Key-phrase extraction from text & various ranking mechanisms (term frequency, heuristics and graph-based co-occurrence);
  • Similarity-based clustering of word embeddings;
  • Using external resources for tag refinement and augmentation (Wordnet lexicon hierarchy).

TagTell approaches for automatic tag extraction using various key-phrase extraction methods.

KEY FEATURES

The TagTell demonstrator provides an interactive platform for automatic tag extraction enabling:

  • Tag extraction, ranking and visualization as a network graph;
  • Tags updates (select, delete, replace tags with user-defined tags);
  • Drill-down to find all documents with a certain tag;
  • Download the data with added tags.

RESEARCH TEAM

  • Jayadeep Kumar Sasikumar, DIT
  • Jimmy Doré , DIT
  • Dr. Tamara Matthews, DIT
  • Prof. Sarah Jane Delany, DIT