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Strategic decision making with exploratory search

Utilize Polecat for intelligent exploratory search; extract valuable insights efficiently from complex information while addressing user queries efficiently. Improve decision-making with Darwinian algorithms.

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Strategic decision making with exploratory search

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  1. Strategic decision making with exploratory search Toby Mostyn CTO Polecat

  2. Agenda What is the point of Polecat? Failing to meet the information need Queries: Handling complex topics Results: Finding “insights” in the noise Solving both problems: an exploratory paradigm Darwinian algorithms

  3. What is the point of Polecat? Intelligent searching on public conversations Unlocking the Potential of Social Media!

  4. Architecture Social media Importer News Blogs Information Extraction Indexing Search platform MeaningMine

  5. What is the point of Polecat? Failing to meet the information need Queries: Handling complex topics Results: Finding “insights” in the noise Solving both problems: an exploratory paradigm Darwinian algorithms

  6. Failing to meet the information need Forming Policy Brand Management What are the issues that people care about most? What/who is my product associated with? Overview Issue Management Briefing I need to know,quickly,all about x Give me an up to the minute / long-term info on an issue

  7. Beyond traditional search Irish Government: setting the agenda for the Irish Economic Forum Query + results = failure to meet information need

  8. What is the point of Polecat? Failing to meet the information need Queries: Handling complex topics Results: Finding “insights” in the noise Solving both problems: an exploratory paradigm Darwinian algorithms

  9. Queries: handling complex topics Information need: What is the discussion around innovation in the UK economy? All (relevant) documents are important! Simple keyword = failure Problem User unable to assess and select keywords User unable to formulate complex boolean query

  10. Queries: handling complex topics Query by document • Feed in 1 to n documents • Pseudo relevance feedback • Query extraction -> query expansion Solution Exploratory interface • Results become query prompts • Users build iterative queries

  11. What is the point of Polecat? Failing to meet the information need Queries: Handling complex topics Results: Finding “insights” in the noise Solving both problems: an exploratory paradigm Darwinian algorithms

  12. Results: Finding “insights” in the noise Goal: provide the user with an exploratory overview of the results Solution: Insights: extracted information/statistics that describe the data • Information Retrieval Statistics • Topic models • Sentiment analysis • Entity extraction Show me the data!

  13. Results: Finding “insights” in the noise

  14. What is the point of Polecat? Failing to meet the information need Queries: Handling complex topics Results: Finding “insights” in the noise Solving both problems: an exploratory paradigm Darwinian algorithms

  15. Solving both problems: an exploratory paradigm

  16. What is the point of Polecat? Failing to meet the information need Queries: Handling complex topics Results: Finding “insights” in the noise Solving both problems: an exploratory paradigm Darwinian algorithms

  17. Darwinian algorithms Which insights are best? Business Polecat Ecosystem Academia How can I best evaluate my algorithm/visualisation?

  18. Darwinian algorithms • Public search application: summarisation engine • Plug-in architecture for 3rd party algorithms/ visualisations • Crowd source judgements • Published evaluation tables (weekly/monthly)

  19. Darwinian algorithms

  20. Darwinian algorithms Ranked insight by query type Ranked insight combinations Ranked visualisation by insight type Individual scores for each contributor

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