Download 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 Drew DeHaas
Outline
 Motivation
 Methodology
 Feature Extraction
 Clustering and Categorizing
 Some Applications
 Conclusion & Exam Questions
Motivation
 A large portion of a company’s data is
unstructured or semi-structured
 Letters
 Emails
 Phone recordings
 Contracts
 Technical documents
 Patents
 Web pages
 Articles
Motivation
 Rapid processing of large document
collections
 Speed!
 Automation of tasks
 Objective analysis
Typical Applications
 Summarizing documents
 Discovering/monitoring relations among
people, places, organizations, etc
 Organizing documents by content
 Indexing for search and retrieval
 Retrieving documents by content
Outline
 Motivation
 Methodology
 Feature Extraction
 Clustering and Categorizing
 Some Applications
 Conclusion & Exam Questions
Methodology: Challenges
 Information is in unstructured textual form
 Natural language interpretation is difficult &
complex task! (not fully possible)
 Text mining deals with huge collections of
documents
Methodology: Two Aspects
 Knowledge Discovery


Extraction of codified information
Mining proper; determining some structure
 Information Distillation

Analysis of feature distribution
Two Text Mining Approaches
 Extraction

Extraction of codified information from single
document
 Analysis

Analysis of the features to detect patterns, trends,
etc, over whole collections of documents
Comparison with Data Mining
 Data mining




Identify data set(s)
Select features
manually
Prepare data
Analyze distribution
 Text mining





Identify documents
Extract features
Select features
(automatically)
Prepare data
Analyze distribution
IBM Intelligent Miner for Text
 IBM introduced product in 1998
 SDK with: Feature extraction, clustering,
categorization, and more
 Traditional components (search engine, etc)
 No longer available?
 The rest of the paper describes text mining
methodology of Intelligent Miner.
Outline
 Motivation
 Methodology
 Feature Extraction
 Clustering and Categorizing
 Some Applications
 Conclusion & Exam Questions
Feature Extraction
 Recognize and classify “significant”
vocabulary items from the text
 Categories of vocabulary





Proper names
Multiword terms
Abbreviations
Relations
Other useful things: numerical forms of
numbers, percentages, money, etc
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
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
Feature Extraction Example
 Disambiguating Proper Names (Nominator
Program)



Apply heuristics to strings, instead of
interpreting semantics
The unit of context for extraction is a document.
The heuristics represent English naming
conventions
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
Outline
 Motivation
 Methodology
 Feature Extraction
 Clustering and Categorizing
 Some Applications
 Conclusion & Exam Questions
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
Two Clustering Engines
 Hierarchical clustering

Orders the clusters into a tree reflecting various
levels of similarity
 Binary relational clustering


Flat clustering
Relationships of different strengths between
clusters, reflecting similarity
Clustering Model
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
Categorization Model
Outline
 Motivation
 Methodology
 Feature Extraction
 Clustering and Categorizing
 Some Applications
 Conclusion & Exam Questions
Applications
 Customer Relationship Management
application provided by IBM Intelligent
Miner for Text called “Customer
Relationship Intelligence”

“Help companies better understand what their
customers want and what they think about the
company itself”
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
Customer Intelligence Usage
 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

Outline
 Motivation
 Methodology
 Feature Extraction
 Clustering and Categorizing
 Some Applications
 Conclusion & Exam Questions
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
Exam Question #1
 Name an example of each of the two main
classes of applications of text mining.


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
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
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?

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.
Questions?