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Text Mining: Finding Nuggets in Mountains of Textual Data

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Limited lexical information (part-of-speech) Avoid analyzing with too much depth ... affect the ability of the program to interpret the semantics of text? ... – PowerPoint PPT presentation

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Title: Text Mining: Finding Nuggets in Mountains of Textual Data


1
Text Mining Finding Nuggets in Mountains of
Textual Data
  • Jochen Dijrre, Peter Gerstl, Roland Seiffert
  • Presented by Huimin Ye

2
Outline
  • Motivation
  • Methodology
  • Feature Extraction
  • Clustering and Categorizing
  • Some Applications
  • Comparison with Data Mining
  • Conclusion Exam Questions

3
Motivation
  • A large portion of a companys data is
    unstructured or semi-structured
  • Letters
  • Emails
  • Phone recordings
  • Contracts
  • Technical documents
  • Patents
  • Web pages
  • Articles

4
Definition
  • Text Mining
  • the discovery by computer of new, previously
    unknown information, by automatically extracting
    information from different written resources

5
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

6
Outline
  • Motivation
  • Methodology
  • Comparison with Data Mining
  • Feature Extraction
  • Clustering and Categorizing
  • Some Applications
  • Conclusion Exam Questions

7
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

8
Methodology Two Aspects
  • Knowledge Discovery
  • Extraction of codified information
  • Mining proper determining some structure
  • Information Distillation
  • Analysis of feature distribution

9
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

10
Outline
  • Motivation
  • Methodology
  • Feature Extraction
  • Clustering and Categorizing
  • Some Applications
  • Comparison with Data Mining
  • Conclusion Exam Questions

11
IBM Intelligent Miner for Text
  • IBM introduced Intelligent Miner for Text in 1998
  • SDK with Feature extraction, clustering,
    categorization, and more
  • Traditional components (search engine, etc)
  • The rest of the paper describes text mining
    methodology of Intelligent Miner.

12
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

13
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

14
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

15
Advantages to IBMs approach
  • Processing is very fast (helps when dealing with
    huge amounts of data)
  • Heuristics work reasonably well
  • Generally applicable to any domain

16
Outline
  • Motivation
  • Methodology
  • Comparison with Data Mining
  • Feature Extraction
  • Clustering and Categorizing
  • Some Applications
  • Conclusion Exam Questions

17
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

18
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

19
Clustering Model
20
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

21
Categorization Model
22
Outline
  • Motivation
  • Methodology
  • Feature Extraction
  • Clustering and Categorizing
  • Some Applications
  • Comparison with Data Mining
  • Conclusion Exam Questions

23
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

24
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

25
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

26
Outline
  • Motivation
  • Methodology
  • Feature Extraction
  • Clustering and Categorizing
  • Some Applications
  • Comparison with Data Mining
  • Conclusion Exam Questions

27
Comparison with Data Mining
  • Text mining
  • Discover hidden facts.
  • tries to understand the details, cross reference
    between individual instances
  • biosciences, customer profile analysis
  • Data mining
  • Discover hidden models.
  • tries to generalize all of the data into a single
    model.
  • marketing, medicine, health care

28
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
    IBMs Intelligent Miner for Text

29
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

30
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

31
Exam Question 3
  • In the Nominator program of IBMs 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.

32
Questions?
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