This document discusses text analytics in enterprise search. It begins with an introduction to text analytics and its benefits, including reducing the time spent searching for and reading documents. Examples of text analytics applications like entity extraction, document categorization, and summarization are provided. Solution techniques for each example using frameworks like Solr, Mallet, and custom Solr request handlers are then outlined. The document concludes that text analytics can bring structure to unstructured content and enhance discovery, relevance, and productivity.

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
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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.
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4. About Text Analytics
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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
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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
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7. Types of Applications
• Entity Extraction
• Document Categorization
• Sentiment Analysis
• Summarization
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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
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9. Benefits and possibilities
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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
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11. Examples
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12. Entity Extraction
 Types of Entities for Extraction:
• Dates
• Places
• Companies
• Objects (Product names, etc)
• People
• Events
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13. Example – Presenting the data
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14. Example – Presenting the data
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15. Example – Facets on the data
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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)
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17. Example Solution: Entity Extraction
 Register a custom class to lookup resources and extract found entities
to specific Solr fields, setup in solrconfig.xml:
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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.
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19. Example – Facets from Categories
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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.
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21. Example Solution: Document
Categorization
 Mallet and the process of setup and train:
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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)
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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.
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24. Example – Summarize content
Static Summaries
Dynamic Summaries
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25. Example – Summarize content - 1
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26. Example – Summarize content - 2
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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)
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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.
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29. Questions ?
daniel.ling@findwise.com
www.findabilityblog.com
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