Text Analytics in Enterprise Search - Daniel Ling
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The document discusses text analytics in enterprise search, outlining its definition, benefits, and applications such as entity extraction, document categorization, and summarization. It highlights challenges like the prevalence of unstructured data and emphasizes improvements in information discovery and findability. The author, Daniel Ling, provides insights into various frameworks and techniques, exemplifying how these can enhance user efficiency and content analysis in enterprises.
Text Analytics in Enterprise Search - Daniel Ling
- 1. Text Analytics in Enterprise Search Daniel Ling (Findwise)
- 2. What will I cover? Intro About Text Analytics Benefits and possibilities Examples Solution Techniques to Examples Conclusions 3
- 3. My Background Daniel Ling Findwise Enterprise Search and Findability Consultant Experience and expertise 5+ years of Enterprise Search Experience 20+ enterprise search implementations, ranging industries Lucene, FAST ESP, Solr Apache Solr my primary search platform Focus areas includes Findability and Search Architecture and Implementation, Text Analytics, Document Processing. 4
- 4. About Text Analytics 5
- 5. Text Analytics in the Enterprise Challenges: 80% of data in the Enterprise is unstructured. Reduce the time looking for information (currently 9.6 hours per week) Reduce the time reading documents / e-mails (currently 14.5 hours per week) Benefits: More predictable scale and domain Well-understood domain Supporting content for analytics can be identified 6
- 6. Text Analytics The definition A set of linguistic, statistical and machine learning techniques used to model and structure information content of textual source. - Wikipedia.org 7
- 7. Types of Applications • Entity Extraction • Document Categorization • Sentiment Analysis • Summarization 8
- 8. Frameworks and Techniques Framework Techniques Solr Statistics, Lingustics Mallet, Classifier4j, etc, etc.. Statistical natural language processing Mahout (Hadoop) Machine Learning, Statistics GATE General language processing framework UIMA Content analytics, text mining, pipeline OpenNLP Machine learning toolkit for NLP 9
- 10. Benefits and possibilities Text analytics can bring some structure to the unstructured content Enhance discovery and findability of content • Works well together with search Increase relevance and precision with extracted keywords and meta- data Generating content for dynamic pages / topic pages • Selection of documents and extracts from documents Track and discover sentiments Reduce the time for user to analyze content 11
- 11. Examples 12
- 12. Entity Extraction Types of Entities for Extraction: • Dates • Places • Companies • Objects (Product names, etc) • People • Events 13
- 13. Example – Presenting the data 14
- 14. Example – Presenting the data 15
- 15. Example – Facets on the data 16
- 16. Example Solution: Entity Extraction Rule-based entity extraction Combination of lists and regular expressions Works within well-understood domains. Requires maintaining lists. Lists from: Country lists from World Factbook, Public Companies from Google Finance, Customers from CRM. Workflow: Document for indexing > Update Request Handler > Update Chain (lookup and match entities) > Writes to index Update Chain (processor) Lucene Index (lists | input fields | entity fields) (entity fields) 17
- 17. Example Solution: Entity Extraction Register a custom class to lookup resources and extract found entities to specific Solr fields, setup in solrconfig.xml: 18
- 18. Document Categorization To assign a label to the document / content / data. Labels for the category or for the sentiment. Threshold values for matching a category before labeling. Statistics and “knowledge” from previous examples can be used. 19
- 19. Example – Facets from Categories 20
- 20. Example Solution: Document Categorization * Training the component, Mallet (Machine Learning for Language Toolkit). • Alternative components includes Lucene (TFIDF) index (MoreLikeThis), OpenNLP, Textcat, Classifier4j. Running the new documents against the model/index of trained documents. Training from interface, adhoc, or index pre-categorized. * Figure from the book Taming Text. 21
- 21. Example Solution: Document Categorization Mallet and the process of setup and train: 22
- 22. Example Solution: Document Categorization Evaluation of new document: Setting the evaluated category tag to the document in pipeline: Update Chain (processor) Lucene Index (input document) (category field) 23
- 23. Document Summarization Summarize a document, at index time or on-demand. Leverage from the knowledge and term statistics of the document and the index. Picks the “most important” sentences based on the statistics and displays those. 24
- 24. Example – Summarize content Static Summaries Dynamic Summaries 25
- 25. Example – Summarize content - 1 26
- 26. Example – Summarize content - 2 27
- 27. Example Solution: Document Summarization Custom RequestHandler that receives document ID and field to summarize. Custom Search Component making the selection of top sentences. Selecting a subset of sentences and sends these back in a field. RequestHandler Lucene Index (SearchComponent for summariziation) 28
- 28. Wrap Up • Examples: Entity Extraction, Document Categorization, Summarization. • Technology: You can take small steps and get a great deal of gain, since you can leverage from features and components of Solr and Lucene (as well as other open source NLP frameworks). • Value: Benefits from text analytics includes the increase in discovery, findability and productivity from the solution. 29