The document discusses text analytics, emphasizing its role in extracting meaningful insights from unstructured data, which constitutes about 80% of corporate information. It highlights techniques such as natural language processing and the challenges involved in disambiguation and information extraction, while noting the significant growth of digital information. The future of text analytics is portrayed as evolving towards summarization, generalization, and narrative generation.

1. Michael Ferretti
Tony Russell-Rose
Vladimir Zelevinsky
Text Analytics:
Yesterday
Today
Tomorrow

2. 2
Part 1 (of 3)
WHAT?

3. 3
What is Text Analytics?
 A set of linguistic, analytical and predictive techniques to extract
structure and meaning extracted from unstructured documents
– Text Analytics ~= Natural Language Processing ~= Text Mining
– Text Mining → Scientific / technical context, automated processing
– Text Analytics → Business context, interactive apps
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“NLP”
vs.
“Text Analytics”

4. 4
Why is Text Analytics Important?
 ‘80% of corporate information is unstructured’
– Entire value chain for some organisation (media / publishing etc.)
– Retail / eCommerce: Product reviews
– User generated content: blogs, forums, wikis
– Voice of the Customer: social media + sentiment analysis
 161 billion gigabytes of digital information in 2006
– approximately 988 exabytes by 2010
– Audio / video still needs summaries & tags etc.
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5. 5
How Difficult Can It Be?
 As humans we do it effortlessly ... don’t we?
 DRUNK GETS NINE YEARS IN VIOLIN CASE
 PROSTITUTES APPEAL TO POPE
 STOLEN PAINTING FOUND BY TREE
 RED TAPE HOLDS UP NEW BRIDGE
 DEER KILL 300,000
 RESIDENTS CAN DROP OFF TREES
 INCLUDE CHILDREN WHEN BAKING COOKIES
 MINERS REFUSE TO WORK AFTER DEATH
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6. 6
Some Fundamentals
 Language is AMBIGUOUS
– To find structure, we must remove ambiguity!
 Lexical analysis (tokenisation)
– The cat sat on the mat
– I can’t tokenise this sentence
 Morphology (term variations, prefixes, suffixes, etc.)
– Computer, computing, compute, computed = comput*
– Delegate = de-leg-ate (?)
– Ratify = rat-ify (?)
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7. 7
More Fundamentals
 Syntax (part of speech tagging)
– Time flies like an arrow
– Fruit flies like a banana
– Eats shoots and leaves
 Parsing (grammar)
– I saw a venetian blind
– I saw a blind venetian
– Rugby is a game played by men with odd-shaped balls
 Sentence boundary detection
– Punctuation denotes the end of a sentence!
– “But not always!”, said Fred...
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8. 8
Named Entity Recognition/Information Extraction
 Companies in New York != New companies in York
 People, places, organisations ...
– Increase precision
– Support navigation
– Facilitate translation, summarisation, speech synthesis, etc.
 IE = template filling
– Entities + relationships
– Highly context dependent
 Problems with:
– Anaphora resolution
– Word sense disambiguation
Copyright ©2010 Endeca Technologies, Inc. All rights reserved. Proprietary and confidential.

9. 9
Question Answering
 Give me answers, not documents!
 Fact-finding vs. exploratory search
– Yes/no questions ‘Is George W. Bush the current president of the
USA?’
– ‘Who’ questions ‘Who was the British Prime Minister before Margaret
Thatcher?’
– List questions ‘Which football teams have won the Champions
League this decade?
– Instruction-based questions ‘How do I cook lasagne?’
– Explanation questions ‘Why did World War I start?’
– Commands ‘Tell me the height of the Eiffel Tower.’
 Question analysis → document retrieval → answer extraction
Copyright ©2010 Endeca Technologies, Inc. All rights reserved. Proprietary and confidential.

10. 10
Part 2 (of 3)
HOW?

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Text Analytics is Computer Science + Semantics.
Semantics is the study of meaning.
Definitions
Universal flowchart:

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No mind reading (yet). Have to use text.
Text approximates meaning.
Meaning is structured.
Meaning and structure
CONCEPT CONCEPT CONCEPT

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Synonymy:
one concept maps to different words.
Polysemy:
one word maps to different concepts.
Problems with text

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Simplest structure: salient terms
Many years later, as he faced the firing squad, Colonel
Aureliano Buendía was to remember that distant afternoon
when his father took him to discover ice.
– Marquez (1962)

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Typed entities
People, places, organizations; etc.
Simple approach: word lists.
More difficult: trained extractors (including sentiment).

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Highest clarity organizations for “baseball”
Top terms World Series Teams
1987 Cardinals; Twins Cardinals; Twins
1988 Dodgers; Mets Dodgers; Athletics
1989 Athetics; Giants Athetics; Giants
1991 Braves; Twins Braves; Twins
1992 Blue Jays; Braves Blue Jays; Braves
1996 Yankees; Braves Yankees; Braves
1997 Indians; Marlins Indians; Marlins
1998 Yankees; Padres Yankees; Padres
1999 Braves; Mets; Yankees Braves; Yankees
2000 Yankees; Mets Yankees; Mets
2001 Diamondbacks; Yankees Diamondbacks; Yankees
2003 Marlins Marlins; Yankees
Fail: 1990, 1993, 1995, 2002.

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Salient terms on a timeline: baseball
No event in 1994!
Clarity scores for top terms for “baseball” search:

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Salient terms on a timeline: Iraq

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Excellent corpus:
Research articles.
Written by humans.
Tagged by authors.
Case study: ACM
But:
Half the articles untagged.
Tags sparse (90% of tags used once!)
Synonyms abound.
tags → controlled tag vocabulary → high-scoring salient tags

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Co-occurrence:
salient terms that tend to occur together belong together.
Clusters

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Clusters for disambiguation

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Apple, meaning 1

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Apple, meaning 2

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Apple, meaning 3

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Human brain is great at extracting information scent:
[word, word, word, …] → meaning
Information Scent
[island, Indonesia] [code, Sun] [coffee, beans, brew] → Java

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Vector model
– Salton (1983)
Similarity between documents = cosine of the angle between their vectors
Can also rotate basis for the best representation: LSI

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Semantic networks
emotion →

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Custom Dimensions

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Custom Dimensions

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Sentence structure parsing

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It is said Mrs. Clinton promises new jobs will be created by her.
N V V N N V A N V V V N
part of speech tagging
noun / verb phrase extraction
sentence structure analysis
anaphora resolution
passive tense flipping
triple filtering
hierarchy generation
Sentence structure parsing

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Nouns by head noun:
[Mrs. + Hillary + Bill + President]
→ Clinton
Verbs by hypernyms (broadening synonyms):
[say + tell + propose + suggest + declare]
→ express
Hierarchy generation (also semantic network!)

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Idea Navigation

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Idea Navigation

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Idea Navigation

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Idea Navigation

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Part 3 (of 3)
WHO?

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 For profit:
– Lexalytics
 Text Enrichment module
 Text Enrichment with Sentiment Analysis
– Alias-i
 Term Discovery
– Nstein
 Newssift
 For fun:
– GATE (Sheffield University)
 Open source, linguistic focus
– RapidMiner (University of Dortmund / Rapid-I)
 Open source community edition, data mining focus
– WordNet, OpenCalais, LingPipe, NLPwiki, etc.
Text Analytics for fun and profit
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40. 40
 Market maturing & expanding: 75-200%
– Most vendors on target
 Dominant markets:
– CX, media/publishing, FS & insurance, intelligence, life sciences, e-
discovery
 Solutions still not standardized
– Need for self-service tuning & configuration
 Massive expansion in social media
– Lightweight NLP for buzz analysis, brand monitoring, etc.
 Partner ecosystem developing
– Marketing services providers, platform vendors, CRM + call centre
vendors, system integrators
Market Outlook: 2010
Copyright ©2010 Endeca Technologies, Inc. All rights reserved. Proprietary and confidential.

41. 41
Conclusions
What do we expect in the future?
 Extraction leads to generation
 Summarization
 Generalization
 Narratives
 Inference and conflict resolution
We are all interested in the future, for that is where you and I
are going to spend the rest of our lives. And remember, my
friend, future events such as these will affect you in the future.
– Edward Wood Jr. (1957)

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Text analytics: what does it mean?
Unstructured text isn't unstructured. There's always structure.
Find the information scent. Let the users follow it.
Don’t trust that one query is enough. Let the users interact.
Text does not matter. Meaning does.