Text Tango: A New Text Data Mining Project

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Text TangoA New Text Data Mining Project

• Marti A. Hearst
• GUIR Meeting, Sept 17, 1998

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Talk Outline

• What is Data Mining?
• What isnt Text Data Mining?
• What is Text Data Mining
• Examples
• A proposal for a system for Text Data Mining

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What is Data Mining? (Fayyad Uthurusamy 96,
Fayyad 97)

• Fitting models to or determining patterns from
  very large datasets.
• A regime which enables people to interact
  effectively with massive data stores.
• Deriving new information from data.
• finding patterns across large datasets
• discovering heretofore unknown information

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What is Data Mining?

• Potential point of confusion
• The extracting ore from rock metaphor does not
  really apply to the practice of data mining
• If it did, then standard database queries would
  fit under the rubric of data mining
• Find all employee records in which employee earns
  300/month less than their managers
• In practice, DM refers to
• finding patterns across large datasets
• discovering heretofore unknown information

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DM Touchstone Applications(CACM 39 (11) Special
Issue)

• Finding patterns across data sets
• Reports on changes in retail sales
• to improve sales
• Patterns of sizes of TV audiences
• for marketing
• Patterns in NBA play
• to alter, and so improve, performance
• Deviations in standard phone calling behavior
• to detect fraud
• for marketing

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What is Text Data Mining?

• Peoples first thought
• Make it easier to find things on the Web.
• This is information retrieval!
• The metaphor of extracting ore from rock does
  make sense for extracting documents of interest
  from a huge pile.
• But does not reflect notions of DM in practice
• finding patterns across large collections
• discovering heretofore unknown information

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Text DM ! IR

• Data Mining
• Patterns, Nuggets, Exploratory Analysis
• Information Retrieval
• Finding and ranking documents that match users
  information need
• ad hoc query
• filtering/standing query

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Real Text DM

• What would finding a pattern across a large text
  collection really look like?

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Bill Gates MS-DOS in the Bible!
From The Internet Diary of the man who
cracked the Bible Code Brendan McKay,
Yahoo Internet Life, www.zdnet.com/yil
(William Gates, agitator, leader)
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From The Internet Diary of the man who cracked
the Bible Code Brendan McKay, Yahoo Internet
Life, www.zdnet.com/yil
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Real Text DM

• The point
• Discovering heretofore unknown information is not
  what we usually do with text.
• (If it werent known, it could not have been
  written by someone.)
• However
• There are some interesting problems of this type!

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Combining Data Typesfor Novel Tasks

• Text Links to find authority pages
  (Kleinberg at Cornell, Page at Stanford)
• Usage Time Links to study evolution of web
  and information use (Pitkow et al. at PARC)

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Ore-Filled Text Collections

• Congressional Voting Records
• Answer questions like
• Who are the most hypocritical congresspeople?
• Medical Articles
• Create hypotheses about causes of rare diseases
• Create hypotheses about gene function
• Patent Law
• Answer questions like
• Is government funding of research worthwhile?

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How to find Hypocritical Congresspersons?

• This must have taken a lot of work
• Hand cutting and pasting
• Lots of picky details
• Some people voted on one but not the other bill
• Some people share the same name
• Check for different county/state
• Still messed up on Bono
• Taking stats at the end on various attributes
• Which state
• Which party

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How to find causes of disease?Don Swansons
Medical Work

• Given
• medical titles and abstracts
• a problem (incurable rare disease)
• some medical expertise
• find causal links among titles
• symptoms
• drugs
• results

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Swanson Example (1991)

• Problem Migraine headaches (M)
• stress associated with M
• stress leads to loss of magnesium
• calcium channel blockers prevent some M
• magnesium is a natural calcium channel blocker
• spreading cortical depression (SCD)implicated in
  M
• high levels of magnesium inhibit SCD
• M patients have high platelet aggregability
• magnesium can suppress platelet aggregability
• All extracted from medical journal titles

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Swansons TDM

• Two of his hypotheses have received some
  experimental verification.
• His technique
• Only partially automated
• Required medical expertise
• Few people are working on this.

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How to find functions of genes?

• Important problem in molecular biology
• Have the genetic sequence
• Dont know what it does
• But
• Know which genes it coexpresses with
• Some of these have known function
• So Infer function based on function of
  co-expressed genes
• This is new work by Michael Walker and others at
  Incyte Pharmaceuticals

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Gene Co-expressionRole in the genetic pathway
Kall.
Kall.
g?
h?
PSA
PSA
PAP
PAP
g?
Other possibilities as well
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Make use of the literature

• Look up what is known about the other genes.
• Different articles in different collections
• Look for commonalities
• Similar topics indicated by Subject Descriptors
• Similar words in titles and abstracts
• adenocarcinoma, neoplasm, prostate, prostatic
  neoplasms, tumor markers, antibodies ...

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Developing Strategies

• Different strategies seem needed for different
  situations
• First see what is known about Kallikrein.
• 7341 documents. Too many
• AND the result with disease category
• If result is non-empty, this might be an
  interesting gene
• Now get 803 documents
• AND the result with PSA
• Get 11 documents. Better!

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Developing Strategies

• Look for commalities among these documents
• Manual scan through 100 category labels
• Would have been better if
• Automatically organized
• Intersections of important categories scanned
  for first

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Try a new tack

• Researcher uses knowledge of field to realize
  these are related to prostate cancer and
  diagnostic tests
• New tack intersect search on all three known
  genes
• Hope they all talk about diagnostics and prostate
  cancer
• Fortunately, 7 documents returned
• Bingo! A relation to regulation of this cancer

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Formulate a Hypothesis

• Hypothesis mystery gene has to do with
  regulation of expression of genes leading to
  prostate cancer
• New tack do some lab tests
• See if mystery gene is similar in molecular
  structure to the others
• If so, it might do some of the same things they
  do

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Strategies again

• In hindsight, combining all three genes was a
  good strategy.
• Store this for later
• Might not have worked
• Need a suite of strategies
• Build them up via experience and a good UI

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The System

• Doing the same query with slightly different
  values each time is time-consuming and tedious
• Same goes for cutting and pasting results
• IR systems dont support varying queries like
  this very well.
• Each situation is a bit different
• Some automatic processing is needed in the
  background to eliminate/suggest hypotheses

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The System

• Three main parts
• UI for building/using strategies
• Backend for interfacing with various databases
  and translating different formats
• Content analysis/machine learning for figuring
  out good hypotheses/throwing out bad ones

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The UI part

• Need support for building strategies
• Lots of info lying around, so a nice option is
  ...
• Two-handed interface
• Big table display
• Mixed-initiative system
• Trade off between user-initiated hypotheses
  exploration and system-initiated suggestions
• Information visualization
• Another way to show lots of choices

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Candidate Associations
Suggested Strategies
Current Retrieval Results
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Other applications

• Patent example
• Political example
• The truths out there!

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Text Tango

• Just starting up now.
• Let me know if youd like to work on it!