Recording – Best Practices for Large Scale Text Mining Process

Best Practices for Large Scale Text Mining Process

This webinar has been recorded and available on demand.

Large textual collections are precious source of information which are hard to organise and access due to their unstructured and heterogeneous nature. With the help of text mining and text analytics we can facilitate the information extraction that mines this hidden knowledge.

In this webinar, Ivelina Nikolova, PhD, shared best practices and text analysis examples from successful text mining process in domains like news, financial and scientific publishing, pharma industry and cultural heritage.

These best practices in text mining are derived from 15+ years of experience and are proven in commercial projects in organisation of various size and industry, from large enterprises to startups.

Join this webinar to learn:

  • What to consider when describing your text mining problem
  • How to define your text mining process so that you get the results you expect
  • What text mining techniques to chose
  • What are the prerequisites for a successful text miningdomain knowledge, examples, good definition of extracted data, etc.
  • DOs and DON’Ts in setting up a text mining process.
  • Industry applications that maximise Return on Investment (ROI) of your text mining processe.g. increased user engagement via content recommendations, shortened research cycle via semantic search, regulatory compliance via smart indexing, etc.

What to expect from a text mining project?  

The extracted structured data from unstructured text, as important facts, key-phrases and entities of interest, is the basis for efficient document indexing, improved search, contextualized and personalized recommendations and many more business-specific analyses. Moreover, automation and advanced semantic technology are important part of the formula that opens new opportunities and scales on large volume of data at now affordable cost.

Ivelina Nikolova – Senior NLP Engineer

Ivelina Nikolova has experience in various projects for semantic textual enrichment and document classification. Her main expertise is in the area of information extraction such as named entity recognition, event recognition and relation extraction with hybrid methods combining rule-based and machine learning techniques.

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