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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 l.jpg

Learning Subjective Adjectives From Corpora

Janyce M. Wiebe

New Mexico State University

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


Introduction l.jpg
Introduction

  • Learning evaluation and opinion clues

    • Distributional similarity process

    • Refinement with lexical features

    • Improved results from both


Subjectivity tagging l.jpg
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 l.jpg
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 l.jpg

“Enthused”

“Wonderful!”

“Great product”

?

“Speculated”

“Maybe”

Types

“Complained”

“You Idiot!”

“Terrible product”


Subjectivity l.jpg

“Enthused”

“Wonderful!”

“Great product”

Subjectivity

?

“Complained”

“You Idiot!”

“Terrible product”

“Speculated”

“Maybe”


Subjectivity7 l.jpg
Subjectivity

Same word, different types

“Great majority” objective

“Great!“ positive evaluative

“Just great.” negative evaluative


Subjectivity8 l.jpg
Subjectivity

Multiple types, sources, targets

“It’s the best!”, he gushed.

- +

Writer He It


Applications flame recognition l.jpg

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 l.jpg
Review Mining WARRINER!!!!

From: Hoodoo>hoodooBUGZAPPER@newnorth.net>

Newsgroups: rec.gardens

Subject: Re: Garden software

I bought a copy of Garden Encyclopedia from Sierra.

Well worth the time and money.


Information extraction l.jpg
Information Extraction WARRINER!!!!

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 l.jpg
Other Applications WARRINER!!!!

  • Clustering documents by ideology

  • Text summarization

  • Style in machine translation and generation


Overview l.jpg
Overview WARRINER!!!!

  • 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 l.jpg

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 l.jpg
Adjectives WSJ sentences

  • 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 l.jpg

R2 WSJ sentences

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 l.jpg
Lin’s Distributional Similarity WSJ sentences

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 l.jpg
Bizarre WSJ sentences

strange similar scary unusual fascinating

interesting curious tragic different

contradictory peculiar silly sad absurd

poignant crazy funny comic compelling

odd


Bizarre19 l.jpg
Bizarre WSJ sentences

strange similar scary unusual fascinating

interesting curious tragic different

contradictory peculiar silly sad absurd

poignant crazy funny comic compelling

odd


Bizarre20 l.jpg
Bizarre WSJ sentences

strange similar scary unusual fascinating

interesting curious tragic different

contradictory peculiar silly sad absurd

poignant crazy funny comic compelling

odd


Bizarre21 l.jpg
Bizarre WSJ sentences

strange similar scary unusual fascinating

interesting curious tragic different

contradictory peculiar silly sad absurd

poignant crazy funny comic compelling

odd


Slide22 l.jpg
Good WSJ sentences

bad better best nice poor

terrific great decent lousy

dismal excellent positive exciting

fantastic marvelous strong

important dumb fair healthy


Slide23 l.jpg
Good WSJ sentences

bad better best nice poor

terrific great decent lousy

dismal excellent positive exciting

fantastic marvelous strong

important dumb fair healthy


Experiments l.jpg

9 WSJ sentences

10

Experiments


Experiments25 l.jpg

Seeds + WSJ sentences

Similar

Words

9

10

Experiments

Distributional

similarity

Separate

corpus

Seeds


Experiments26 l.jpg

Seeds + WSJ sentences

Similar

Words

9

10

p(subjective | s)

S

Words

Experiments

Distributional

similarity

Separate

corpus

Seeds

Filtering

S > Adj > Majority


Lexical features l.jpg
Lexical features WSJ sentences

  • Polarity and Gradability

  • Learned from corpora

    • Statistical processing informed by linguistic insights

    • Different data sets used


Gradability l.jpg

Norm WSJ sentencesLarge Larger Largest

Gradability

* More additional

* Very additional

Hatzivassiloglou & Wiebe 2000


Polarity l.jpg

+ polarity WSJ sentences

beautiful

object

- polarity

ugly

Polarity

Corrupt and brutal

* Corrupt but brutal

Hatzivassiloglou & McKeown 1997


Experiments30 l.jpg

Separate WSJ sentences

corpus

Seeds +

Similar

Words

9

10

Experiments

Distributional

similarity

Seeds

Filtering


Experiments31 l.jpg

Separate WSJ sentences

corpus

Separate

corpus

Seeds +

Similar

Words

9

10

Experiments

Distributional

similarity

Seeds

Filtering

Lexical

Classification


Results l.jpg

Lex Seed Lex WSJ sentences

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 l.jpg
Results WSJ sentences

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 l.jpg
Future Work WSJ sentences

  • Apply process to Netnews and Listservs

  • Apply word-sense disambiguation techniques to potentially subjective expressions

  • Flame recognition and review mining


Conclusions l.jpg
Conclusions WSJ sentences

  • Learning linguistic knowledge from corpora for a pragmatic task

  • Linguistic information

    • Manual annotation

    • Linguistic constraints

  • Processes improve each other


Application 1 flame recognition l.jpg
Application 1: Flame recognition WSJ sentences

From: pattreck@aol.com (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 l.jpg
Flames (continued) WSJ sentences

From: Suzanne <Suzanne_member@newsguy.com>

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 l.jpg
Likely WSJ sentences

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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