Article | Proceedings of LREC 2016 Workshop. Resources and Processing of Linguistic and Extra-Linguistic Data from People with Various Forms of Cognitive/Psychiatric Impairments (RaPID-2016), Monday 23rd of May 2016 | Detecting semantic changes in Alzheimer’s disease with vector space models
Göm menyn

Title:
Detecting semantic changes in Alzheimer’s disease with vector space models
Author:
Kathleen C. Fraser: Department of Computer Science, University of Toronto, Toronto, Canada Graeme Hirst: Department of Computer Science, University of Toronto, Toronto, Canada
Download:
Full text (pdf)
Year:
2016
Conference:
Proceedings of LREC 2016 Workshop. Resources and Processing of Linguistic and Extra-Linguistic Data from People with Various Forms of Cognitive/Psychiatric Impairments (RaPID-2016), Monday 23rd of May 2016
Issue:
128
Article no.:
001
Pages:
1 to 8
No. of pages:
8
Publication type:
Abstract and fulltext
Published:
2016-06-03
ISBN:
978-91-7685-730-4
Series:
Linköping Electronic Conference Proceedings
ISSN (print):
1650-3686
ISSN (online):
1650-3740
Publisher:
Linköping University Electronic Press, Linköpings universitet


Export in BibTex, RIS or text

Numerous studies have shown that language impairments, particularly semantic deficits, are evident in the narrative speech of people with Alzheimer’s disease from the earliest stages of the disease. Here, we present a novel technique for capturing those changes, by comparing distributed word representations constructed from healthy controls and Alzheimer’s patients. We investigate examples of words with different representations in the two spaces, and link the semantic and contextual differences to findings from the Alzheimer’s disease literature.

Keywords: distributional semantics, Alzheimer’s disease, narrative speech

Proceedings of LREC 2016 Workshop. Resources and Processing of Linguistic and Extra-Linguistic Data from People with Various Forms of Cognitive/Psychiatric Impairments (RaPID-2016), Monday 23rd of May 2016

Author:
Kathleen C. Fraser, Graeme Hirst
Title:
Detecting semantic changes in Alzheimer’s disease with vector space models
References:

Adlam, A.-L. R., Bozeat, S., Arnold, R., Watson, P., and Hodges, J. R. (2006). Semantic knowledge in mild cognitive impairment and mild Alzheimer’s disease. Cortex, 42(5):675–684.


Ahmed, S., de Jager, C. A., Haigh, A.-M., and Garrard, P. (2013). Semantic processing in connected speech at a uniformly early stage of autopsy-confirmed Alzheimer’s disease. Neuropsychology, 27(1):79.


Appell, J., Kertesz, A., and Fisman, M. (1982). A study of language functioning in Alzheimer patients. Brain and language, 17(1):73–91.


Baroni, M., Dinu, G., and Kruszewski, G. (2014). Don’t count, predict! A systematic comparison of contextcounting vs. context-predicting semantic vectors. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (ACL), pages 238–247.


Beach, T. G., Monsell, S. E., Phillips, L. E., and Kukull,W. (2012). Accuracy of the clinical diagnosis of Alzheimer disease at National Institute on Aging Alzheimer Disease Centers, 2005–2010. Journal of Neuropathology & Experimental Neurology, 71(4):266–273.


Becker, J. T., Boiler, F., Lopez, O. L., Saxton, J., and McGonigle, K. L. (1994). The natural history of Alzheimer’s disease: description of study cohort and accuracy of diagnosis. Archives of Neurology, 51(6):585–594.


Bird, S., Klein, E., and Loper, E. (2009). Natural Language Processing with Python. O’Reilly Media.


Breedin, S. D., Saffran, E. M., and Schwartz, M. F. (1998). Semantic factors in verb retrieval: An effect of complexity. Brain and Language, 63:1–31.


Bullinaria, J. A. and Levy, J. P. (2012). Extracting semantic representations from word co-occurrence statistics: stop-lists, stemming, and SVD. Behavior Research Methods, 44(3):890–907.


Chenery, H. J. and Murdoch, B. E. (1994). The production of narrative discourse in response to animations in persons with dementia of the Alzheimer’s type: Preliminary findings. Aphasiology, 8(2):159–171.


Croisile, B., Ska, B., Brabant, M.-J., Duchene, A., Lepage, Y., Aimard, G., and Trillet, M. (1996). Comparative study of oral and written picture description in patients with Alzheimer’s disease. Brain and language, 53(1):1–19.


Firth, J. R. (1957). A synopsis of linguistic theory. Studies in Linguistic Analysis, pages 1–32.


Forbes-McKay, K. E. and Venneri, A. (2005). Detecting subtle spontaneous language decline in early Alzheimer’s disease with a picture description task. Neurological Sciences, 26:243–254.


Fraser, K. C., Meltzer, J. A., and Rudzicz, F. (2015). Linguistic features identify Alzheimer’s disease in narrative speech. Journal of Alzheimer’s Disease, 49(2):407–422.


Giffard, B., Desgranges, B., Nore-Mary, F., Lalev´ee, C., de la Sayette, V., Pasquier, F., and Eustache, F. (2001). The nature of semantic memory deficits in Alzheimer’s disease. Brain, 124(8):1522–1532.


Giles, E., Patterson, K., and Hodges, J. R. (1996). Performance on the Boston Cookie Theft picture description task in patients with early dementia of the Alzheimer’s type: missing information. Aphasiology, 10(4):395–408.


Goodglass, H. and Kaplan, E. (1983). Boston diagnostic aphasia examination booklet. Lea & Febiger Philadelphia, PA.


Guo, J., Che, W., Wang, H., and Liu, T. (2014). Learning sense-specific word embeddings by exploiting bilingual resources. In Proceedings of the 25th International Conference on Computational Linguistics (COLING), pages 497–507.


Hakkani-T¨ur, D., Vergyri, D., and T¨ur, G. (2010). Speechbased automated cognitive status assessment. In Proceedings of the 11th Annual Conference of the International Speech Communication Association (INTERSPEECH), pages 258–261.


Huang, E. H., Socher, R., Manning, C. D., and Ng, A. Y. (2012). Improving word representations via global context and multiple word prototypes. In Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics, pages 873–882. Association for Computational Linguistics.


Kempler, D. (1995). Language changes in dementia of the Alzheimer type. Dementia and Communication, pages 98–114.


Kim, M. and Thompson, C. K. (2004). Verb deficits in Alzheimer’s disease and agrammatism: Implications for lexical organization. Brain and Language, 88(1):1–20.


Kirshner, H. S. (2012). Primary progressive aphasia and Alzheimer’s disease: brief history, recent evidence. Current neurology and neuroscience reports, 12(6):709–714.


Lira, J. O. d., Minett, T. S. C., Bertolucci, P. H. F., and Ortiz, K. Z. (2014). Analysis of word number and content in discourse of patients with mild to moderate Alzheimer’s disease. Dementia & Neuropsychologia, 8(3):260–265.


MacWhinney, B. (2000). The CHILDES project: Tools for analyzing talk: Volume I: Transcription format and programs. Lawrence Erlbaum Associates.


MacWhinney, B. (2007). The Talkbank Project. In Beal, J., Corrigan, K., and Moisl, H. L., editors, Creating and Digitizing Language Corpora, pages 163–180. Springer.


Manning, C. D., Raghavan, P., Sch¨utze, H., et al. (2008). Introduction to Information Retrieval, volume 1. CambridgeUniversity Press.


Mikolov, T., Chen, K., Corrado, G., and Dean, J. (2013). Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781.


Monsch, A. U., Bondi, M. W., Butters, N., Salmon, D. P., Katzman, R., and Thal, L. J. (1992). Comparisons of verbal fluency tasks in the detection of dementia of the Alzheimer type. Archives of Neurology, 49(12):1253–1258.


Nicholas, M., Obler, L. K., Albert, M. L., and Helm-Estabrooks, N. (1985). Empty speech in Alzheimer’s disease and fluent aphasia. Journal of Speech, Language, and Hearing Research, 28(3):405–410.


Pakhomov, S. V., Smith, G. E., Chacon, D., Feliciano, Y., Graff-Radford, N., Caselli, R., and Knopman, D. S. (2010). Computerized analysis of speech and language to identify psycholinguistic correlates of frontotemporal lobar degeneration. Cognitive and Behavioral Neurology, 23:165–177.


Perry, R. J. and Hodges, J. R. (1999). Attention and executive deficits in Alzheimer’s disease. Brain, 122(3):383–404.


Reilly, J., Troche, J., and Grossman, M. (2011). Language processing in dementia. The handbook of Alzheimer’s disease and other dementias, pages 336–368.


Reisinger, J. and Mooney, R. J. (2010). Multi-prototype vector-space models of word meaning. In Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), pages 109–117. Association for Computational Linguistics.


Rousseeuw, P. J. (1987). Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 20:53–65.


Salmon, D. P., Butters, N., and Chan, A. S. (1999). The deterioration of semantic memory in Alzheimer’s disease. Canadian Journal of Experimental Psychology, 53(1):108.


Snowdon, D. A., Kemper, S. J., Mortimer, J. A., Greiner, L. H., Wekstein, D. R., and Markesbery, W. R. (1996). Linguistic ability in early life and cognitive function and Alzheimer’s disease in late life: findings from the Nun Study. Journal of the American Medical Association, 275(7):528–532.


Socher, R., Huval, B., Manning, C. D., and Ng, A. Y. (2012). Semantic compositionality through recursive matrix-vector spaces. In Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pages 1201–1211. Association for Computational Linguistics.


Van der Maaten, L. and Hinton, G. (2008). Visualizing data using t-SNE. Journal of Machine Learning Research, 9:2579–2605.


Van Dongen, S. and Enright, A. J. (2012). Metric distances derived from cosine similarity and Pearson and Spearman correlations. arXiv preprint arXiv:1208.3145.


Wu, Z. and Giles, C. L. (2015). Sense-aaware semantic analysis: A multi-prototype word representation model using wikipedia. In Proceedings of the 29th AAAI Conference on Artificial Intelligence (AAAI-15), pages 2188–2194.


Zou, W. Y., Socher, R., Cer, D. M., and Manning, C. D. (2013). Bilingual word embeddings for phrase-based machine translation. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1393–1398.


Zuccon, G., Azzopardi, L. A., and Van Rijsbergen, C. (2009). Semantic spaces: Measuring the distance between different subspaces. In The Proceedings of the 3rd International Symposium on Quantum Interaction, pages 225–236.

Proceedings of LREC 2016 Workshop. Resources and Processing of Linguistic and Extra-Linguistic Data from People with Various Forms of Cognitive/Psychiatric Impairments (RaPID-2016), Monday 23rd of May 2016

Author:
Kathleen C. Fraser, Graeme Hirst
Title:
Detecting semantic changes in Alzheimer’s disease with vector space models
Note: the following are taken directly from CrossRef
Citations:
No citations available at the moment


Responsible for this page: Peter Berkesand
Last updated: 2017-02-21