Transcript 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 Kenneth Rockwood Medicine, Dalhousie University Computer Science Dalhousie University Elissa Asp English Deparment Saint Mary
PUL Workshop, Dalhousie University, Halifax, 23 Apr 2004
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s University
1
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 5
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 7
Related work: Automated authorship attribution
Problem of identifying the author of an anonymous text .
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One of Text Categorization Problems Spam detection Language and encoding identification Authorship attribution and plagiarism detection Text genre classification Topic detection Sentiment classification 8
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 11
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 _th 0.015
___ 0.013
L=5 rle ley ey_ y_w sort by frequency the 0.013
he_ 0.011
and 0.007
_an 0.007
_wa was … nd_ 0.007
ed_ 0.006
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How do we compare two profiles?
Dickens: A Tale of Two Cities Dickens: Christmas Carol _th 0.015
___ 0.013
the 0.013
he_ 0.011
and 0.007
?
_th 0.016
the 0.014
he_ 0.012
and 0.007
nd_ 0.007
?
Carroll: Alice’s adventures in wonderland _th 0.017
___ 0.017
the 0.014
he_ 0.014
ing 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 2.00E-03 1.50E-03 1.00E-03 5.00E-04 0.00E+00 1 4 7 10 13 16 19 22 25 28 31 34 6-grams
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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
1 (
n
)
f
1 (
n
) 2
f
2 (
n
)
f
2 (
n
) 2
n
profile 2 (
f
1
f
1 (
n
) (
n
)
f
2
f
2 ( (
n
)
n
)) 2 weight 15
Authorship Attribution Evaluation
100 90 80 70 60 50 40 30 20 10 0 English Greek A Greek B Greek B+ Chinese Style Lang. M CNG
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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 18
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 non Alzheimer • accuracy is reported 19
Experiment 1.1
training and testing part (90:10) use all speakers to generate profiles use both interviews 20
ACADIE: Classification accuracy
L = 20 50 100 200 500 1000 1500 2000 3000 4000 5000 n=1 88% 73% 73% 73% 73% 73% 73% 73% 73% 73% 73% 2 85% 80% 78% 93% 80% 95% 98% 98% 98% 98% 98% 3 83% 4 88% 5 98% 6 93% 7 95% 8 80% 9 85% 10 85% 78% 95% 98% 98% 95% 95%
100%
95% 98% 98% 85% 98% 93%
100%
93% 98% 95% 98%
100% 100% 100% 100% 100% 100% 100%
95%
100% 100%
98% 98%
100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%
93% 93% 98% 98%
100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%
98%
100% 100% 100% 100% 100% 100%
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Improvement detection
S a S b
similarity similarity with Alzheimer profile with non Alzheimer profile
S
S a S
a S b
normalized similarity with Alzheimer profile (0.5
threshold ) improvement is detected by observing an increase in S value between the first and second interview 22
ACADIE: Detected improvement
L = 20 50 100 200 500 1000 1500 2000 3000 4000 5000 n=1 50% 50% 40% 40% 40% 40% 40% 40% 40% 40% 40% 2 60% 70% 60% 30% 80% 50% 70% 60% 60% 60% 60% 3 70% 60% 40% 30% 60% 90% 80% 90% 70% 70% 70% 4 80% 30% 40% 40% 80% 60% 70% 70% 70% 90% 80% 5 70% 60% 40% 50% 60% 70% 80% 70% 70% 80% 80% 6 50% 30% 40% 70% 50% 70% 60% 70% 60% 80% 70% 7 50% 30% 80% 40% 40% 70% 80% 70% 60% 70% 60% 8 40% 60% 60% 70% 60% 90% 80% 70% 70% 60% 70% 9 60% 50% 70% 50% 80% 60% 60% 60% 60% 70% 70% 10 50% 70% 60% 60% 70% 60% 50% 60% 70% 70% 70% 23
Experiment 1.2
use only first interviews to create Alzheimer and Non-Alzheimer profiles 24
Exp. 1.2: Classification accuracy
L = 20 50 100 200 500 1000 1500 2000 3000 4000 5000 n=1 85% 70% 73% 73% 73% 73% 73% 73% 73% 73% 73% 2 85% 90% 98% 88% 83% 95% 95% 95% 95% 95% 95% 3 83% 4 88% 5 93% 6 90% 7 95% 8 80% 9 80% 10 83% 83% 98% 98% 98% 98% 98% 95% 90%
100% 100% 100%
95% 85% 98% 98% 98% 93% 98%
100%
95% 95% 98% 95%
100%
95% 95% 90% 98%
100% 100%
98%
100% 100% 100% 100% 100% 100% 100% 100% 100%
95%
100% 100% 100% 100% 100% 100% 100%
93% 95%
100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%
98%
100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100%
Improvement detection: 0.6-0.9
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Experiment 1.3
use only first interviews only speech produced by patients, caregivers, and other (not field researchers) 26
Exp. 1.3: Classification accuracy
L = 20 50 100 200 500 1000 1500 2000 3000 4000 5000 n=1 75% 73% 73% 73% 73% 73% 73% 73% 73% 73% 73% 2 90% 88% 85% 83% 65% 83% 78% 80% 83% 83% 83% 3 85% 68% 88% 90%
95%
93% 80% 75% 83% 90% 93% 4 80% 5 65% 6 75% 80% 85% 90%
95%
90% 88%
95% 95%
75% 80% 88%
95%
93%
95% 95% 98% 98%
93%
95% 100% 95% 100%
88%
95% 95% 95% 98% 98% 95% 98%
7 80% 75% 83% 93%
98% 98% 98% 98% 95% 98% 98%
8 70% 75% 88% 88% 90%
98% 98% 98% 98% 98% 98%
9 75% 80% 88%
98%
93%
98% 98% 98% 95% 95% 98%
10 70% 83% 88% 93%
95% 95% 95% 95%
93%
95%
93% Improvement detection: 0.6-0.8
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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 Alzheimer non-Alzheimer n-gram rank 28
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 29
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 20.5 24.5 30 four classes: severe moderate mild normal two classes: low high 31
MMSE Score distribution in data set
severe moderate mild normal 32
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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 1.
2.
set of attributes MA-A: attributes similar to BSCW: 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 add TTR and Honore statistics Brunet statistic shown to be non-significant 7 POS attributes selected (conjunctions included) use several machine learning algorithms with cross-validation, using software tool WEKA 35
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Ordinal CNG Method
• use two extreme groups to build profiles normal level severe dementia level profile severe profile normal CNG similarity:
S severe S normal
test speech profile classify according to
S S severe severe
S normal
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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) 38
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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 41