Untangling Text Data Mining

Marti Hearst UC Berkeley SIMS

ACL’99 Plenary TalkJune 23, 1999

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 HaystacksThe 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

Real Text DM

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

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

Candidate Associations

Current Retrieval Results

Suggested Strategies

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!