1. Text Tango: A New Text Data Mining Project
Marti A. Hearst
GUIR Meeting, Sept 17, 1998

2. Talk Outline What is Data Mining? What isn’t Text Data Mining?
What is Text Data Mining
Examples
A proposal for a system for Text Data Mining
Marti A. Hearst
UC Berkeley SIMS 1998

3. 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
Marti A. Hearst
UC Berkeley SIMS 1998

4. 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
Marti A. Hearst
UC Berkeley SIMS 1998

5. 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
Marti A. Hearst
UC Berkeley SIMS 1998

6. 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
Marti A. Hearst
UC Berkeley SIMS 1998

7. Text DM != IR Data Mining: Information Retrieval:
Patterns, Nuggets, Exploratory Analysis
Information Retrieval:
Finding and ranking documents that match users’ information need
ad hoc query
filtering/standing query
Marti A. Hearst
UC Berkeley SIMS 1998

8. Real Text DM
What would finding a pattern across a large text collection really look like?
Marti A. Hearst
UC Berkeley SIMS 1998

9. Bill Gates + MS-DOS in the Bible!
From: “The Internet Diary of the man who cracked the Bible Code”
Brendan McKay, Yahoo Internet Life,
(William Gates, agitator, leader)
Marti A. Hearst
UC Berkeley SIMS 1998

10. From: “The Internet Diary of the man who cracked the Bible Code”
Brendan McKay, Yahoo Internet Life,
Marti A. Hearst
UC Berkeley SIMS 1998

11. Real Text DM The point: However:
Discovering heretofore unknown information is not what we usually do with text.
(If it weren’t known, it could not have been written by someone.)
However:
There are some interesting problems of this type!
Marti A. Hearst
UC Berkeley SIMS 1998

12. Combining Data Types for 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)
Marti A. Hearst
UC Berkeley SIMS 1998

13. 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
Is government funding of research worthwhile?
Marti A. Hearst
UC Berkeley SIMS 1998

14. Marti A. Hearst
UC Berkeley SIMS 1998

15. Marti A. Hearst
UC Berkeley SIMS 1998

16. 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
Marti A. Hearst
UC Berkeley SIMS 1998

17. How to find functions of genes?
Important problem in molecular biology
Have the genetic sequence
Don’t 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
Marti A. Hearst
UC Berkeley SIMS 1998

18. Gene Co-expression: Role in the genetic pathway
Kall.
Kall. g? h?
PSA
PSA
PAP
PAP g?
Other possibilities as well
Marti A. Hearst
UC Berkeley SIMS 1998

19. 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 ...
Marti A. Hearst
UC Berkeley SIMS 1998

20. 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!
Marti A. Hearst
UC Berkeley SIMS 1998

21. 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
Marti A. Hearst
UC Berkeley SIMS 1998

22. 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
Marti A. Hearst
UC Berkeley SIMS 1998

23. 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
Marti A. Hearst
UC Berkeley SIMS 1998

24. 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
Marti A. Hearst
UC Berkeley SIMS 1998

25. 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 don’t 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
Marti A. Hearst
UC Berkeley SIMS 1998

26. 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
Marti A. Hearst
UC Berkeley SIMS 1998

27. The UI part Mixed-initiative system Information visualization
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
Marti A. Hearst
UC Berkeley SIMS 1998

28. Candidate Associations
Suggested Strategies
Current Retrieval Results
Marti A. Hearst
UC Berkeley SIMS 1998

29. Other applications Patent example Political example
The truth’s out there!
Marti A. Hearst
UC Berkeley SIMS 1998

30. Text Tango Just starting up now.
Let me know if you’d like to work on it!
Marti A. Hearst
UC Berkeley SIMS 1998