Transcript Text Mining: Finding Nuggets in Mountains of Textual Data

Text Mining: Finding Nuggets in
Mountains of Textual Data
Jochen Dijrre, Peter Gerstl, Roland Seiffert
Presented by Ebrahim Kobeissi
Text in this color are my additions/modifications
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Outline
 Motivation
 Methodology
 Feature Extraction
 Clustering and Categorizing
 Some Applications
 Comparison with Data Mining
 Conclusion & Exam Questions
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Motivation
 A large portion of a company’s data is
unstructured or semi-structured
 Letters
 Emails
 Phone transcripts
 Contracts
 Technical documents
 Patents
 Web pages
 Articles
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Definition
 Text Mining:

the discovery by computer of new, previously
unknown information, by automatically
extracting information from different written
resources
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Typical Applications
 Summarizing documents
 Discovering/monitoring relations among people,
places, organizations, etc
 Customer profile analysis
 Trend analysis
 Documents summarization
 Spam Identification
 Public health early warning
 Event tracks
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Outline
 Motivation
 Methodology
 Comparison with Data Mining
 Feature Extraction
 Clustering and Categorizing
 Some Applications
 Conclusion & Exam Questions
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Methodology: Challenges
 Information is in unstructured textual form
 Natural language interpretation is difficult &
complex task! (not fully possible)

Watson is a step closer
 Text mining deals with huge collections of
documents

Impossible for human examination
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Methodology: Two Aspects
 Knowledge Discovery
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Extraction of codified information
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Feature Extraction
Mining proper; determining some structure
 Information Distillation
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Analysis of feature distribution
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Two Text Mining Approaches
 Extraction

Extraction of codified information from single
document
 Analysis
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Analysis of the features to detect patterns, trends,
etc, over whole collections of documents
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Outline
 Motivation
 Methodology
 Feature Extraction
 Clustering and Categorizing
 Some Applications
 Comparison with Data Mining
 Conclusion & Exam Questions
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IBM Intelligent Miner for Text
 IBM introduced Intelligent Miner for Text in 1998
 SDK with: Feature extraction, clustering,
categorization, and more
 Not a drop in application
 Traditional components (search engine, etc)
 The rest of the paper describes text mining
methodology of Intelligent Miner.
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Feature Extraction
 Recognize and classify “significant”
vocabulary items from the text
 Categories of vocabulary
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Proper names – Mrs. Albright or Dheli[sic], India
Multiword terms – Joint venture, online document
Abbreviations – CPU, CEO
Relations – Jack Smith-age-42
Other useful things: numerical forms of numbers,
percentages, money, etc
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Canonical Form Examples
 Normalize numbers, money

Four = 4, five-hundred dollar = $500
 Conversion of date to normal form
 Morphological variants

Drive, drove, driven = drive
 Proper names and other forms

Mr. Johnson, Bob Johnson, The author = Bob
Johnson
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Feature Extraction Approach
 Linguistically motivated heuristics
 Pattern matching
 Limited lexical information (part-of-speech)
 Avoid analyzing with too much depth


Does not use too much lexical information
No in-depth syntactic or semantic analysis
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Advantages to IBM’s approach
 Processing is very fast (helps when dealing
with huge amounts of data)
 Heuristics work reasonably well
 Generally applicable to any domain
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Outline
 Motivation
 Methodology
 Comparison with Data Mining
 Feature Extraction
 Clustering and Categorizing
 Some Applications
 Conclusion & Exam Questions
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Clustering
 Fully automatic process
 Documents are grouped according to
similarity of their feature vectors
 Each cluster is labeled by a listing of the
common terms/keywords
 Good for getting an overview of a document
collection
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Two Clustering Engines
 Hierarchical clustering
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Orders the clusters into a tree reflecting various
levels of similarity
 Binary relational clustering
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Flat clustering
Relationships of different strengths between
clusters, reflecting similarity
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Clustering Model
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Categorization
 Assigns documents to preexisting categories
 Classes of documents are defined by providing a set
of sample documents.
 Training phase produces “categorization schema”
 Documents can be assigned to more than one
category
 If confidence is low, document is set aside for
human intervention
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Categorization Model
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Outline
 Motivation
 Methodology
 Feature Extraction
 Clustering and Categorizing
 Some Applications
 Comparison with Data Mining
 Conclusion & Exam Questions
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Applications
 Customer Relationship Management
application provided by IBM Intelligent
Miner for Text called “Customer
Relationship Intelligence”
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“Help companies better understand what their
customers want and what they think about the
company itself”
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Customer Intelligence Process
 Take as input body of communications with
customer
 Cluster the documents to identify issues
 Characterize the clusters to identify the
conditions for problems
 Assign new messages to appropriate clusters
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Customer Intelligence Usage
 Knowledge Discovery
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Clustering used to create a structure that can be
interpreted
 Information Distillation
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Refinement and extension of clustering results
Interpreting the results
 Tuning of the clustering process
 Selecting meaningful clusters
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Outline
 Motivation
 Methodology
 Feature Extraction
 Clustering and Categorizing
 Some Applications
 Comparison with Data Mining
 Conclusion & Exam Questions
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Comparison with Data Mining
 Data mining
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Discover hidden
models.
tries to generalize all
of the data into a
single model.
marketing, medicine,
health care
 Text mining
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Discover hidden
facts.
tries to understand
the details, cross
reference between
individual instances
biosciences,
customer profile
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analysis
Conclusion
 This paper introduced text mining and how it
differs from data mining proper.
 Focused on the tasks of feature extraction
and clustering/categorization
 Presented an overview of the tools/methods
of IBM’s Intelligent Miner for Text
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Exam Question #1
 What are the two aspects of Text Mining?
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Knowledge Discovery: Discovering a common
customer complaint in a large collection of
documents containing customer feedback.
Information Distillation: Filtering future
comments into pre-defined categories
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Exam Question #2
 How does the procedure for text mining
differ from the procedure for data mining?
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Adds feature extraction phase
Infeasible for humans to select features manually
The feature vectors are, in general, highly
dimensional and sparse
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Exam Question #3
 In the Nominator program of IBM’s Intelligent
Miner for Text, an objective of the design is to
enable rapid extraction of names from large
amounts of text. How does this decision affect the
ability of the program to interpret the semantics of
text?
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Does not perform in-depth syntactic or semantic analysis
of the text; the results are fast but only heuristic with
regards to actual semantics of the text.
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Questions?
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