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Text Analytics in Enterprise Search
 

Text Analytics in Enterprise Search

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Presented by Daniel Ling at Apache Lucene Eurocon 2011 in Barcelona.

Presented by Daniel Ling at Apache Lucene Eurocon 2011 in Barcelona.

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    Text Analytics in Enterprise Search Text Analytics in Enterprise Search Presentation Transcript

    • Text Analytics in Enterprise Search Daniel Ling (Findwise)
    • What will I cover? Intro About Text Analytics Benefits and possibilities Examples Solution Techniques to Examples Conclusions 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
    • About Text Analytics 5
    • Text Analytics in the EnterpriseChallenges: 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
    • Text AnalyticsThe definition A set of linguistic, statistical and machine learning techniques used to model and structure information content of textual source. - Wikipedia.org 7
    • Types of Applications• Entity Extraction• Document Categorization• Sentiment Analysis• Summarization 8
    • Frameworks and TechniquesFramework TechniquesSolr Statistics, LingusticsMallet, Classifier4j, etc, etc.. Statistical natural language processingMahout (Hadoop) Machine Learning, StatisticsGATE General language processing frameworkUIMA Content analytics, text mining, pipelineOpenNLP Machine learning toolkit for NLP 9
    • Benefits and possibilities 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
    • Examples 12
    • Entity Extraction Types of Entities for Extraction: • Dates • Places • Companies • Objects (Product names, etc) • People • Events 13
    • Example – Presenting the data 14
    • Example – Presenting the data 15
    • Example – Facets on the data 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
    • Example Solution: Entity Extraction Register a custom class to lookup resources and extract found entities to specific Solr fields, setup in solrconfig.xml: 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
    • Example – Facets from Categories 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
    • Example Solution: Document Categorization Mallet and the process of setup and train: 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
    • 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
    • Example – Summarize contentStatic SummariesDynamic Summaries 25
    • Example – Summarize content - 1 26
    • Example – Summarize content - 2 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
    • 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
    • Questions ?daniel.ling@findwise.comwww.findabilityblog.com 30