Computational analysis of language used by Alzheimer

Analysis of Spontaneous Speech in Dementia of Alzheimer Type: Experiments with Morphological and Lexical Analysis Nick Cercone Vlado Keselj Calvin Thomas Computer Science Dalhousie University

Kenneth Rockwood Medicine, Dalhousie University Elissa Asp English Deparment Saint Mary’s University

PUL Workshop, Dalhousie University, Halifax, 23 Apr 2004

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Overview  

 

Introduction Related work: Bucks et al, authorship attribution CNG discrimination Pt/other rating dementia levels  



use of attribute sets: MA-A, MA-B CNG and Ordinal CNG

Conclusion 2

Introduction 

Effects of the Alzheimer’s disease (AD)  

 

reduced communicative ability deterioration of linguistic performance

Can we detect it? Current methods rely on structured interviews    



confrontation naming single word production word generation given context word generation given first letter picture description 3

Analysis of spontaneous speech 

drawbacks of structured interviews: 



 



sometimes insensitive to early signs of dementia observed by family low scores are not reliable unless difficulty is observed in natural conversation brake “natural speech” into components subjective, i.e., designed by a researcher

alternative solution: objective automatic analysis of spontaneous, i.e., natural, speech 4

Speech characteristics in Dementia of Alzheimer Type (DAT) 

 



frequent use of functional words (closed class) less rich vocabulary difficulty with constructing longer coherent phrases more difficulties at lexical and morphological level than phonetic and syntactic levels

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Related work: Bucks et al. (BSCW) 

Bucks, Singh, Cuerden, Wilcock 2000, 2001: Analysis of spontaneous conversational speech in dementia of Alzheimer type (DAT)



use eight linguistic measures to analyze transcribed spontaneous speech: 1) noun rate 2) pronoun rate 3) verb rate 4) adjective rate

5) clause-like semantic unit rate (CSU) 6) Brunet’s index (W) 7) token type ratio (TTR) 8) Honore’s statistic (R) 6

Bucks et al.: Experiment design • experiment with 24 participants: • 8 patients and 16 healthy individuals

• discriminating between demented and healthy individuals: • 100% on training data • 87.5% with cross-validation

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Related work: Automated authorship attribution 

Problem of identifying the author of an anonymous text

One of Text Categorization Problems Spam detection Language and encoding identification Authorship attribution and plagiarism detection Text genre classification Topic detection Sentiment classification

. . . . . .

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Related work (authorship attribution) 1. style analysis 

 

using style markers (features) relying on non-trivial NL analysis Stamatatos et al. 2000-02

2. language modeling  

Peng et al. 2003, EACL’03 Khmelev and Teahan 2003, SIGIR’03

3. N-gram-based text categorization 

Cavnar and Trenkle 1994 9

Shortcomings of style analysis • difficult to automatically extract some features • feature selection is critical • language dependent • task dependent, i.e., does not generalize well to other types of classification 10

Character N-gram -based Methods 

Text can be considered as a concatenated sequence of characters instead of words.

Advantages 1. small vocabulary 2. language independence 3. no word segmentation problems in many Asian languages such as Chinese and Thai 

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How do character n-grams work? Marley was dead: to begin with. There is no doubt whatever about that. … (from Christmas Carol by Charles Dickens)

n=3 Mar arl rle ley ey_ y_w _wa was …

sort by frequency

_th ___ the he_ and _an nd_ ed_

0.015 0.013 0.013 0.011 0.007 0.007 0.007 0.006

L=5

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How do we compare two profiles? Dickens: A Tale of Two Cities

Dickens: Christmas Carol

_th ___ the he_ and

0.015 0.013 0.013 0.011 0.007

?

_th the he_ and nd_

?

0.016 0.014 0.012 0.007 0.007

Carroll: Alice’s adventures in wonderland

_th ___ the he_ ing

0.017 0.017 0.014 0.014 0.007

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N-gram distribution (From Dickens: Christmas Carol) 5.00E-03 4.50E-03 4.00E-03 3.50E-03 3.00E-03 2.50E-03

6-grams

2.00E-03 1.50E-03 1.00E-03 5.00E-04

34

31

28

25

22

19

16

13

10

7

4

1

0.00E+00

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CNG profile similarity measure • a profile = the set of L the most frequent n-grams • profile dissimilarity measure:



n profile

  f (n)  f (n) 2  1  f1 ( n )  f 2 ( n )  2 

2

      



n profile

 2  ( f 1 ( n )  f 2 ( n ))   f1 ( n )  f 2 ( n ) 

   

2

weight 15

Authorship Attribution Evaluation 100 90 80 70 60

Style Lang. M CNG

50 40 30 20 10 0 English

Greek A

Greek B Greek B+ Chinese

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ACADIE Data Set • 189 GAS interviews (Goal Attainment Scaling) • 95 patients (2 interviews per patient, except 1 patient) • 6 sites; 17 MB of data (3.2 million words) • interview participants: • FR – field researcher • Pt – patient • Cg – caregiver • other people 17

Experiment set-up • preprocessing • patients divided into two groups • 85 training group (169 interviews) • 10 testing group (20 interviews)

• patient speech in training group is used to build Alzheimer profile • non-patient speech in training group is used to build non-Alzheimer profile • two experiments:

• classification • improvement detection

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Classification • from each test interview patient and non-patient speech is extracted • this produces 40 speech extracts • each speech extract is labelled by the classifier as Alzheimer or nonAlzheimer • accuracy is reported 19

Experiment 1.1   

training and testing part (90:10) use all speakers to generate profiles use both interviews

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ACADIE: Classification accuracy n=1 L = 20 50 100 200 500 1000 1500 2000 3000 4000 5000

88% 73% 73% 73% 73% 73% 73% 73% 73% 73% 73%

2

3

4

5

6

7

8

85% 83% 88% 98% 93% 95% 80% 80% 78% 95% 95% 85% 93% 93% 78% 95% 95% 98% 98% 100% 98% 93% 98% 100% 98% 100% 100% 100% 80% 95% 100% 100% 98% 98% 100% 95% 100% 100% 100% 100% 100% 100% 98% 98% 100% 100% 100% 100% 100% 98% 93% 100% 100% 100% 100% 100% 98% 93% 100% 100% 100% 100% 100% 98% 98% 100% 100% 100% 100% 100% 98% 98% 98% 100% 100% 100% 100%

9

10

85% 95% 98% 100% 100% 100% 100% 100% 100% 100% 100%

85% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%

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Improvement detection S a  similarity

with Alzheimer

S b  similarity

with non - Alzheimer

S 

Sa Sa  Sb

 normalized

Alzheimer

profile

profile profile

similarity

with

(0.5 threshold

)

 improvement is detected by observing an increase in S value between the first and second interview 22

ACADIE: Detected improvement n=1 L = 20 50 100 200 500 1000 1500 2000 3000 4000 5000

50% 50% 40% 40% 40% 40% 40% 40% 40% 40% 40%

2

3

4

5

6

7

8

9

60% 70% 60% 30% 80% 50% 70% 60% 60% 60% 60%

70% 60% 40% 30% 60% 90% 80% 90% 70% 70% 70%

80% 30% 40% 40% 80% 60% 70% 70% 70% 90% 80%

70% 60% 40% 50% 60% 70% 80% 70% 70% 80% 80%

50% 30% 40% 70% 50% 70% 60% 70% 60% 80% 70%

50% 30% 80% 40% 40% 70% 80% 70% 60% 70% 60%

40% 60% 60% 70% 60% 90% 80% 70% 70% 60% 70%

60% 50% 70% 50% 80% 60% 60% 60% 60% 70% 70%

10 50% 70% 60% 60% 70% 60% 50% 60% 70% 70% 70%

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Experiment 1.2 

use only first interviews to create Alzheimer and Non-Alzheimer profiles

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Exp. 1.2: Classification accuracy n=1 L = 20 50 100 200 500 1000 1500 2000 3000 4000 5000

85% 70% 73% 73% 73% 73% 73% 73% 73% 73% 73%

2

3

4

5

6

7

8

85% 83% 88% 93% 90% 95% 80% 90% 83% 98% 95% 85% 93% 95% 98% 98% 98% 90% 98% 98% 98% 88% 98% 100% 100% 98% 100% 95% 83% 98% 100% 95% 98% 95% 100% 95% 100% 100% 100% 100% 100% 100% 95% 95% 100% 100% 100% 100% 100% 95% 93% 100% 100% 100% 100% 100% 95% 95% 100% 100% 100% 100% 100% 95% 98% 100% 100% 100% 100% 100% 95% 100% 100% 100% 100% 100% 100%

9

10

80% 95% 95% 100% 98% 100% 100% 100% 100% 100% 100%

83% 90% 98% 100% 100% 100% 100% 100% 100% 100% 100%

Improvement detection: 0.6-0.9 25

Experiment 1.3  

use only first interviews only speech produced by patients, caregivers, and other (not field researchers)

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Exp. 1.3: Classification accuracy n=1 L = 20 50 100 200 500 1000 1500 2000 3000 4000 5000

75% 73% 73% 73% 73% 73% 73% 73% 73% 73% 73%

2

3

4

5

6

90% 88% 85% 83% 65% 83% 78% 80% 83% 83% 83%

85% 68% 88% 90% 95% 93% 80% 75% 83% 90% 93%

80% 65% 75% 80% 90% 75% 85% 88% 80% 90% 95% 88% 95% 95% 95% 93% 98% 93% 95% 95% 100% 95% 100% 98% 88% 95% 98% 95% 95% 95% 98% 95% 98%

7

8

9

10

80% 75% 83% 93% 98% 98% 98% 98% 95% 98% 98%

70% 75% 88% 88% 90% 98% 98% 98% 98% 98% 98%

75% 80% 88% 98% 93% 98% 98% 98% 95% 95% 98%

70% 83% 88% 93% 95% 95% 95% 95% 93% 95% 93%

Improvement detection: 0.6-0.8 27

Some experiment observations 



Alzheimer n-gram profile captures many indefinite terms and negated (e.g., sometimes, don’t know, can not, …) the profiles captures reduced lexical richness n-gram frequency

Alzheimer non-Alzheimer

n-gram rank

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Second set of experiments 

rating dementia levels



implement method BSCW (by Bucks et al.),



analysis and extension



comparison with CNG



application of a wider set of machine learning algorithms

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MMSE – Mini-Mental State Exam 

   

MMSE – a standard test for identifying cognitive impairment in a clinical setting 17 questions, 5-10 minutes introduced in 1975 by Folstein et al. score range from 0 to 30 a variety of cut points suggested over years: 17.5, 21.5, 23.5, 25.5

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MMSE Score Gradation 

we use the following gradation 0

14.5

four classes: severe two classes:

low

20.5

moderate

24.5

mild

30

normal

high

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MMSE Score distribution in data set severe

moderate mild

normal

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Part-of-speech tagging, MA-A following the BSCW method applied Hepple from NL GATE and Connexor Hepple is based on Brill’s tagger Connexor performed better set of attributes MA-A: attributes similar to BSCW:

 

  

 1. 2.

excluded CSU-rate: manually annotated reported non-significant impact by BSCW 34

Morphological Attribute Set: MA-B start with all POS attributes regression-based attribute selection 7 POS attributes selected (conjunctions included) add TTR and Honore statistics

 



 



Brunet statistic shown to be non-significant

use several machine learning algorithms with cross-validation, using software tool WEKA

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Ordinal CNG Method • use two extreme groups to build profiles normal level

severe dementia level profile severe CNG similarity:

profile normal

Snormal

Ssevere test speech profile



classify according to

S severe S severe  S normal 37

Ordinal CNG: Thresholds 

range of values: [0,1] 

 

0 corresponds to severe, 1 to normal

what are good threshold interesting observation: 

the optimal threshold is very close to the “natural threshold” – 0.5 (varies from 0.5 to 0.512)

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Conclusions 



extensive experiments on morphological and lexical analysis of spontaneous speech for detecting dementia of Alzheimer type methods:  



CNG and Ordinal CNG extension of method proposed by use of POS tags as suggested by BSCW

positive results in classification and detecting dementia level:   



100% discrimination accuracy (Pt and other) 93% - severe/normal 70% - two-class accuracy 46% - four-class accuracy 40

Future work     

improvement detection use of word CNG method stop-word frequency-based classifier syntactic analysis semantic analysis

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