Transcript Text Mining: Finding Nuggets in Mountains of Textual Data

Jochen Dijrre, Peter Gerstl, Roland Seiffert
Presented by Trevor Crum
04/23/2014
*Slides modified from Shamil Mustafayev’s 2013 presentation *
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Outline
Definition and Paper Overview
 Motivation
 Methodology
 Feature Extraction
 Clustering and Categorizing
 Some Applications
 Comparison with Data Mining
 Conclusion & Exam Questions

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Definition

Text Mining:
 The discovery by computer of new, previously
unknown information, by automatically extracting
information from different unstructured textual
documents.
 Also referred to as text data mining, roughly
equivalent to text analytics which refers more
specifically to problems based in a business
settings.
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Paper Overview
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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Outline
Definition and Paper Overview
 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 – about
90% in 1999!
 Letters
 Emails
 Phone transcripts
 Contracts
 Technical documents
 Patents
 Web pages
 Articles
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Typical Applications
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Summarizing documents
Discovering/monitoring relations among
people, places, organizations, etc
Customer profile analysis
Trend analysis
Document summarization
Spam Identification
Public health early warning
Event tracks
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Outline
Definition and Paper Overview
 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)

 Google and Watson are a step closer

Text mining deals with huge collections
of documents
 Impossible for human examination
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Google vs Watson
Google justifies the
answer by returning
the text documents
where it found the
evidence.
 Google finds
documents that are
most suitable to a
given Keyword.
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Watson tries to
understand the
semantics behind
a given key phrase
or question.
Then Watson will
use its huge
knowledge base to
find the correct
answer.
Watson uses more AI
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Methodology: Two Aspects

Knowledge Discovery
 Extraction of codified information
○ Feature Extraction
 Mining proper; determining some structure

Information Distillation
 Analysis of feature distribution
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Two Text Mining Approaches

Extraction
 Extraction of codified information from a
single document

Analysis
 Analysis of the features to detect patterns,
trends, and other similarities over whole
collections of documents
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Outline
Definition and Paper Overview
 Motivation
 Methodology
 Feature Extraction
 Clustering and Categorizing
 Some Applications
 Comparison with Data Mining
 Conclusion & Exam Questions

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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, India
Multiword terms – Joint venture, online document
Abbreviations – CPU, CEO
Relations – Jack Smith-age-42
Other useful things: numerical forms of
numbers, percentages, money, dates, and
many other
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Canonical Form Examples

Normalize numbers, money
 Four = 4, five-hundred dollar = $500
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Conversion of date to normal form
 8/17/1992 = August 18 1992
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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-ofspeech)
 Avoid analyzing with too much depth
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 Does not use too much lexical information
 No in-depth syntactic or semantic analysis
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IBM Intelligent Miner for Text
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IBM introduced Intelligent Miner for Text in
1998
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SDK with: Feature extraction, clustering,
categorization, and more
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Traditional components (search engine, etc)
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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
Definition and Paper Overview
 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
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Hierarchical clustering
 Orders the clusters into a tree reflecting
various levels of similarity
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Binary relational clustering
 Flat clustering
 Relationships of different strengths between
clusters, reflecting similarity
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Clustering Model
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Categorization
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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
Definition and Paper Overview
 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” or CRI
 “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
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Knowledge Discovery
 Clustering used to create a structure that
can be interpreted

Information Distillation
 Refinement and extension of clustering
results
○ Interpreting the results
○ Tuning of the clustering process
○ Selecting meaningful clusters
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Outline
Definition and Paper Overview
 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
 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
analysis
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Outline
Definition and Paper Overview
 Motivation
 Methodology
 Feature Extraction
 Clustering and Categorizing
 Some Applications
 Comparison with Data Mining
 Conclusion & Exam Questions

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Conclusion

Text mining can be used as an effective
business tool that supports
 Creation of knowledge by preparing and
organizing unstructured textual data
[Knowledge Discovery]
 Extraction of relevant information from large
amounts of unstructured textual data
through automatic pre-selection based on
user defined criteria
[Information Distillation]
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Exam Question #1

What are the two aspects of Text Mining
when applied to customer complaints?
 Knowledge Discovery: Discovering a
common customer complaint in a large
collection of documents containing customer
feedback.
 Information Distillation: Filtering future
complaints 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?
 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

What are some examples of unstructured
textual collections used in Text Mining?
 Customer letters
 Email correspondence
 Phone transcripts
 Technical documentation
 Patents
 Many others
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