Outline
Untangling several different fields– DM, CL, IA, TDM
TDM examples TDM as Exploratory Data Analysis
– New Problems for Computational Linguistics– Our current efforts
Classifying Application Types
Patterns Non- NovelNuggets
NovelNuggets
Non- textualdata Standard data
miningDatabasequeries
?
Textual dataComputational
linguisticsI nformation
retrievalReal text
data mining
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.
Why Data Mining? Because the data is there. Because
– larger disks– faster cpus– high-powered visualization – networked information
are becoming widely available.
The Knowledge Discovery from Data Process (KDD)
KDD: The non-trivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data. (Fayyad, Shapiro, & Smyth, CACM 96)
Note: data mining is just one step in the process
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
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
– In practice, DM refers to:» finding patterns across large datasets» discovering heretofore unknown information
What is Text Data Mining?
Many peoples’ first thought: – Make it easier to find things on the Web.– But this is information retrieval!
Needles in Haystacks
The emphasis in IR is in finding documents that already contain answers to questions.
Information RetrievalA restricted form of Information Access
The system has available only pre-existing, “canned” text passages.
Its response is limited to selecting from these passages and presenting them to the user.
It must select, say, 10 or 20 passages out of millions.
What is Text Data Mining?
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
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)
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
Real Text DM
The point:– 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 is a field whose goal is to learn
about patterns in text for their own sake ...
Computational Linguistics!
Goal: automated language understanding– this isn’t possible– instead, go for subgoals, e.g.,
»word sense disambiguation»phrase recognition»semantic associations
Common current approach:– statistical analyses over very large text
collections
Why CL Isn’t TDM
A linguist finds it interesting that “cloying” co-occurs significantly with “Jar Jar Binks” ...
… But this doesn’t really answer a question relevant to the world outside the text itself.
Why CL Isn’t TDM
We need to use the text indirectly to answer questions about the world
Direct:– Analyze patent text; determine which word
patterns indicate various subject categories.
Indirect:– Analyze patent text; find out whether
private or public funding leads to more inventions.
Why CL Isn’t TDM
Direct:– Cluster newswire text; determine which
terms are predominant
Indirect:– Analyze newswire text; gather evidence
about which countries/alliances are dominating which financial sectors
Nuggets vs. Patterns
TDM: we want to discover new information …
… As opposed to discovering which statistical patterns characterize occurrence of known information.
Example: WSD– not TDM: computing statistics over a corpus to
determine what patterns characterize Sense S.– TDM: discovering the meaning of a new sense
of a word.
Nuggets vs. Patterns
Nugget: a new, heretofore unknown item of information.
Pattern: distributions or rules that characterize the occurrence (or non-occurrence) of a known item of information.
Application of rules can create nuggets in some circumstances.
Example: Lexicon Augmentation
Application of a lexico-syntactic pattern:NP0 such as NP1, {NP2 …, (and | or) NPi }
i >= 1, implies thatforall NPi, i>=1, hyponym(NPi, NP0)
Extracts out a new hypernym:– “Agar is a substance prepared from a
mixture of red algae, such as Gelidium, for laboratory or industrial use.”
– implies hyponym(“Gelidium”, “red algae”) However, this fact was already known to
the author of the text.
The Quandry
How do we use text to both– Find new information not known to
the author of the text– Find information that is not about the
text itself
Idea: Exploratory Data Analysis
Use large text collections to gather evidence to support (or refute) hypotheses– Not known to author: links across
many texts– Not self-referential: work within the
domain of discourse
Example: Etiology
Given – medical titles and abstracts– a problem (incurable rare disease)– some medical expertise
find causal links among titles– symptoms– drugs– results
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
How to Automate This?
Idea: mixed-initiative interaction– User applies tools to help explore the
hypothesis space– System runs suites of algorithms to
help explore the space, suggest directions
Our Proposed Approach
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
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
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 abstractsadenocarcinoma, neoplasm, prostate, prostatic
neoplasms, tumor markers, antibodies ...
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!
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
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
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
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
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
The UI part
Need support for building strategies Mixed-initiative system
– Trade off between user-initiated hypotheses exploration and system-initiated suggestions
Information visualization– Another way to show lots of choices
Summary The future: analyzing what the text
is about– We don’t know how; text is tough!– Idea: bring the user into the loop.– Build up piecewise evidence to
support hypotheses– Make use of partial domain models.
The Truth is Out There!
Summary Text Data Mining:
– Extracting heretofore undiscovered information from large text collections
Information Access TDM– IA: locating already known information that
is currently of interest Finding patterns across text is already
done in CL– Tells us about the behavior of language– Helps build very useful tools!