Nick Cercone Vlado Keselj Calvin Thomas Computer Science Dalhousie University

1. 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

2. 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

3. 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

4. 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

5. 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

6. 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 5) clause-like semantic unit rate 2) pronoun rate (CSU) 3) verb rate 6) Brunet’s index (W) 4) adjective rate 7) token type ratio (TTR) 8) Honore’s statistic (R)

7. 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

8. One of Text Categorization Problems • Spam detection • Language and encoding identification • Authorship attribution and plagiarism detection • Text genre classification • Topic detection • Sentiment classification Related work: Automated authorship attribution • Problem of identifying the author of an anonymous text

9. Related work (authorship attribution) • style analysis • using style markers (features) • relying on non-trivial NL analysis • Stamatatos et al. 2000-02 • language modeling • Peng et al. 2003, EACL’03 • Khmelev and Teahan 2003, SIGIR’03 • N-gram-based text categorization • Cavnar and Trenkle 1994

10. 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

11. 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

12. 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 _th 0.015 L=5 ___ 0.013 arl rle the 0.013 ley he_ 0.011 sort by frequency ey_ and 0.007 y_w _an 0.007 _wa nd_ 0.007 ed_ 0.006 was …

13. How do we compare two profiles? Dickens: A Tale of Two Cities _th 0.016 Dickens: Christmas Carol the 0.014 ? _th 0.015 he_ 0.012 ___ 0.013 and 0.007 the 0.013 nd_ 0.007 he_ 0.011 Carroll: Alice’s adventures in wonderland and 0.007 _th 0.017 ___ 0.017 the 0.014 ? he_ 0.014 ing 0.007

14. N-gram distribution (From Dickens: Christmas Carol)

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

16. Authorship Attribution Evaluation

17. 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

18. 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

19. 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

20. Experiment 1.1 • training and testing part (90:10) • use all speakers to generate profiles • use both interviews

21. ACADIE: Classification accuracy

22. Improvement detection • improvement is detected by observing an increase in S value between the first and second interview

23. ACADIE: Detected improvement

24. Experiment 1.2 • use only first interviews to create Alzheimer and Non-Alzheimer profiles

25. Exp. 1.2: Classification accuracy Improvement detection: 0.6-0.9

26. Experiment 1.3 • use only first interviews • only speech produced by patients, caregivers, and other (not field researchers)

27. Exp. 1.3: Classification accuracy Improvement detection: 0.6-0.8

28. 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 n-gram frequency non-Alzheimer n-gram rank

29. 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

30. 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

31. 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

32. MMSE Score distribution in data set severe moderate mild normal

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

35. 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

37. Ordinal CNG Method • use two extreme groups to build profiles normal level severe dementia level profile severe profile normal Snormal CNG similarity: Ssevere test speech profile • classify according to

38. 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)

40. 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

41. Future work • improvement detection • use of word CNG method • stop-word frequency-based classifier • syntactic analysis • semantic analysis