learning subjective adjectives from corpora
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Learning Subjective Adjectives From Corpora Janyce M. Wiebe New Mexico State University Office of Naval Research grant N00014-95-1-0776. Introduction Learning evaluation and opinion clues Distributional similarity process Refinement with lexical features Improved results from both

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learning subjective adjectives from corpora

Learning Subjective Adjectives From Corpora

Janyce M. Wiebe

New Mexico State University

Office of Naval Research grant N00014-95-1-0776.

introduction
Introduction
  • Learning evaluation and opinion clues
    • Distributional similarity process
    • Refinement with lexical features
    • Improved results from both
subjectivity tagging
Subjectivity Tagging

Recognizing opinions and evaluations

(Subjective sentences) as opposed to

material objectively presented as true

(Objective sentences)

Banfield 1985, Fludernik 1993, Wiebe 1994

examples
Examples
  • At several different levels, it’s a fascinating tale. subjective
  • Bell Industries Inc. increased its quarterly to 10 cents from 7 cents a share.

objective

types

“Enthused”

“Wonderful!”

“Great product”

?

“Speculated”

“Maybe”

Types

“Complained”

“You Idiot!”

“Terrible product”

subjectivity

“Enthused”

“Wonderful!”

“Great product”

Subjectivity

?

“Complained”

“You Idiot!”

“Terrible product”

“Speculated”

“Maybe”

subjectivity7
Subjectivity

Same word, different types

“Great majority” objective

“Great!“ positive evaluative

“Just great.” negative evaluative

subjectivity8
Subjectivity

Multiple types, sources, targets

“It’s the best!”, he gushed.

- +

Writer He It

applications flame recognition

R [ 13: Julia ] Re: BILL WARRINER!!!!

R [ 19: Suzanne ] Re: BILL WARRINER!!!!

RS < 16: Suzanne > Re: BILL WARRINER!!!!

R [ 26: Doug Bone & Jacqui D] Re: A bin full of buds

R [ 24: Karin Adamczyk ] Rose hips?

R [ 88: Colette Tremblay ] Re: Rose hips? (long)

R [ 8: Karin Adamczyk ]

R [ 29: Kay Cangemi ] Re: Rose hips?

R [ 23: Karin Adamczyk ]

R [ 30: Karin Adamczyk ]

R [ 18: BCD ] Re: red(as in wine red) roses

R [ 32: Laura Johnson-Kelly ]

RS [ 3: PattReck ]

R [ 27: Bugman ] Re: BILL WARRINER!!!!

R [ 37: Bill ]

R < 41: Celeste >

Applications: Flame Recognition
review mining
Review Mining

From: Hoodoo>[email protected]>

Newsgroups: rec.gardens

Subject: Re: Garden software

I bought a copy of Garden Encyclopedia from Sierra.

Well worth the time and money.

information extraction
Information Extraction

Northwest Airlines settled the remaining lawsuits, a federal judge said. objective

“The cost of health care is eroding our standard of living and sapping industrial strength,” complains Maher. subjective

other applications
Other Applications
  • Clustering documents by ideology
  • Text summarization
  • Style in machine translation and generation
overview
Overview
  • Identify large set of candidate clues
  • Existing resources are not sufficient
    • Not consistently marked for subjectivity
    • Not customized to the genre
  • Learn lexical clues from corpora
corpus and annotation
Subjectivity tags assigned by multiple annotators to 1001 WSJ sentences

Tags representing consensus opinions obtained with EM

Corpus and Annotation

Wiebe et al. 1999; Bruce & Wiebe 1999

adjectives
Adjectives
  • Classifications correlated with adjectives
  • Adjectives extracted from annotations

They promised [e+ 2 yet] more for

[e+ 3 reallygood] [e? 1 stuff].

"It\'s [e? 3 really] [e- 3 bizarre]," says Albert Lerman, creative director at the Wells agency.

lin s distributional similarity

R2

R3

I

have

a

brown

dog

R1

R4

Lin’s Distributional Similarity

Word R W

I R1 have

have R2 dog

brown R3 dog

. . .

Lin 1998

lin s distributional similarity17
Lin’s Distributional Similarity

Word1

Word2

R W R W R W R W

R W R W R W R W

R W R W

R W R W

bizarre
Bizarre

strange similar scary unusual fascinating

interesting curious tragic different

contradictory peculiar silly sad absurd

poignant crazy funny comic compelling

odd

bizarre19
Bizarre

strange similar scary unusual fascinating

interesting curious tragic different

contradictory peculiar silly sad absurd

poignant crazy funny comic compelling

odd

bizarre20
Bizarre

strange similar scary unusual fascinating

interesting curious tragic different

contradictory peculiar silly sad absurd

poignant crazy funny comic compelling

odd

bizarre21
Bizarre

strange similar scary unusual fascinating

interesting curious tragic different

contradictory peculiar silly sad absurd

poignant crazy funny comic compelling

odd

slide22
Good

bad better best nice poor

terrific great decent lousy

dismal excellent positive exciting

fantastic marvelous strong

important dumb fair healthy

slide23
Good

bad better best nice poor

terrific great decent lousy

dismal excellent positive exciting

fantastic marvelous strong

important dumb fair healthy

experiments25

Seeds +

Similar

Words

9

10

Experiments

Distributional

similarity

Separate

corpus

Seeds

experiments26

Seeds +

Similar

Words

9

10

p(subjective | s)

S

Words

Experiments

Distributional

similarity

Separate

corpus

Seeds

Filtering

S > Adj > Majority

lexical features
Lexical features
  • Polarity and Gradability
  • Learned from corpora
    • Statistical processing informed by linguistic insights
    • Different data sets used
gradability

Norm Large Larger Largest

Gradability

* More additional

* Very additional

Hatzivassiloglou & Wiebe 2000

polarity

+ polarity

beautiful

object

- polarity

ugly

Polarity

Corrupt and brutal

* Corrupt but brutal

Hatzivassiloglou & McKeown 1997

experiments30

Separate

corpus

Seeds +

Similar

Words

9

10

Experiments

Distributional

similarity

Seeds

Filtering

experiments31

Separate

corpus

Separate

corpus

Seeds +

Similar

Words

9

10

Experiments

Distributional

similarity

Seeds

Filtering

Lexical

Classification

results

Lex Seed Lex

Pol+ + 4.6 +10.8

Pol- +18.5 +18.7

Pol+,- + 8.5 +11.8

Grad+ + 6.5 +15.3

Results

Seed +7.5

results33
Results

Lex Seed Lex

Pol+,Grad+ + 6.4 +18.0

Pol-, Grad+ +19.9 +21.4

Pol+,- Grad+ + 8.4 +18.2

Seed +7.5

future work
Future Work
  • Apply process to Netnews and Listservs
  • Apply word-sense disambiguation techniques to potentially subjective expressions
  • Flame recognition and review mining
conclusions
Conclusions
  • Learning linguistic knowledge from corpora for a pragmatic task
  • Linguistic information
    • Manual annotation
    • Linguistic constraints
  • Processes improve each other
application 1 flame recognition
Application 1: Flame recognition

From: [email protected] (PattReck)

Newsgroups: rec.gardens.roses

Subject: Re: red(as in wine red) roses

My two favorite old reds: Cramoisi Superieure, especially great

climbing, and Francis Dubreuil. Also Prospero does well in

southern California - aren\'t you on the west coast? -- Candace

flames continued
Flames (continued)

From: Suzanne <[email protected]>

Newsgroups: rec.gardens.roses

Subject: Re: BILL WARRINER!!!!

>>Wow. You guys are really working poor Suzanne over.

>po thang. I thank she been workin over her bottle of Kahlua.

***Up &^%$!!! I\'ve been working at a *job* - no Kahlua! You are a

snow-snorting dust-bowl dweller, the dustiest of the dusties. Bill

Bradley has the support of the "environmentalists" ha ha ha!

likely
Likely

likely possible willing probable

receptive unlikely able logical

rumored potential counterproductive

moot significant hesitant worthy

unwilling probably desirable

weak forthcoming imminent

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