Propositional Idea Density in aphasic discourse

Bryant, Lucy; Spencer, Elizabeth; Ferguson, Alison; Craig, Hugh; Colyvas, Kim; Worrall, Linda

doi:10.1080/02687038.2013.803514

Cited by 23 publications

(26 citation statements)

References 25 publications

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“…Kemper et al reported PD for Nun Study participants with AD to be 0.434 reporting a difference of 0.101 compared to their healthy participants (Kemper, Greiner, et al, 2001). Differences in PD of people with aphasia following stroke (mean PD ¼ 0.455) and healthy matched participants (mean PD ¼ 0.547) has been reported indicating a statistically significant difference of 0.092 (Bryant et al, 2013). However, the confounds of text length found in the present study and intra-individual variation found by Spencer et al (2012) would appear to be important to explore in studies investigating differences between clinical and healthy populations.…”

Section: Main Findingsmentioning

confidence: 85%

“…This is an important step in the process of developing an understanding of what is typical in ageing and therefore providing a reference point against which we can begin to assess clinical populations. The clinical application of PD has been piloted in a study by Bryant et al (2013) who investigated PD in oral interviews of 40 people with aphasia and their non-aphasic family members (n ¼ 49). This research found that PD measures for people with aphasia were lower than those found for family members and that lower PD was correlated with severity of aphasia (Bryant et al, 2013).…”

Section: Implications For Future Researchmentioning

confidence: 99%

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Propositional idea density in older men’s written language: Findings from the HIMS study using computerised analysis

Spencer

Ferguson

Craig

et al. 2014

Clinical Linguistics & Phonetics

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Decline in linguistic function has been associated with decline in cognitive function in previous research. This research investigated the informativeness of written language samples of Australian men from the Health in Men's Study (HIMS) aged from 76 to 93 years using the Computerised Propositional Idea Density Rater (CPIDR 5.1). In total, 60,255 words in 1147 comments were analysed using a linear-mixed model for statistical analysis. Results indicated no relationship with education level (p = 0.79). Participants for whom English was not their first learnt language showed Propositional Idea Density (PD) scores slightly lower (0.018 per 1 word). Mean PD per 1 word for those for whom English was their first language for comments below 60 words was 0.494 and above 60 words 0.526. Text length was found to have an effect (p = <0.0001). The mean PD was higher than previously reported for men and lower than previously reported for a similar cohort for Australian women.

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Section: Main Findingsmentioning

confidence: 85%

Section: Implications For Future Researchmentioning

confidence: 99%

Propositional idea density in older men’s written language: Findings from the HIMS study using computerised analysis

Spencer

Ferguson

Craig

et al. 2014

Clinical Linguistics & Phonetics

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“…For example, metrics related to vocabulary size, such as TTR and word-frequencies, have proven to help with early detection of mild cognitive impairment (MCI) (Aramaki et al, 2016), hence are important for early dementia diagnosis. Discourse informativeness, measured via propositional idea density, is also shown to be significantly affected in speakers with aphasia (Bryant et al, 2013). Furthermore, lexicon-based methods have proved to be successful in sentiment analysis (Taboada et al, 2011;Tang et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Lexical Features Are More Vulnerable, Syntactic Features Have More Predictive Power

Novikova

Balagopalan

Shkaruta

et al. 2019

Proceedings of the 5th Workshop on Noisy User-Generated Text (W-Nut 2019)

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Understanding the vulnerability of linguistic features extracted from noisy text is important for both developing better health text classification models and for interpreting vulnerabilities of natural language models. In this paper, we investigate how generic language characteristics, such as syntax or the lexicon, are impacted by artificial text alterations. The vulnerability of features is analysed from two perspectives: (1) the level of feature value change, and (2) the level of change of feature predictive power as a result of text modifications. We show that lexical features are more sensitive to text modifications than syntactic ones. However, we also demonstrate that these smaller changes of syntactic features have a stronger influence on classification performance downstream, compared to the impact of changes to lexical features. Results are validated across three datasets representing different text-classification tasks, with different levels of lexical and syntactic complexity of both conversational and written language.

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“…While Kintsch & Keenan (1973) looked at the influence of propositional idea density on reading times and recall for both individual sentences as well as short paragraphs, work since the 1970s has been limited to the level of multiple sentences and used primarily as an indicator of cognitive deficits (Ferguson et al, 2014;Bryant et al, 2013;Farias et al, 2012;Riley et al, 2005). This paper returns to the examination of idea density's applicability for individual sentences.…”

Section: Theories Of Online Sentence Processingmentioning

confidence: 99%

Psycholinguistic Models of Sentence Processing Improve Sentence Readability Ranking

Howcroft

Demberg

2017

Proceedings of the 15th Conference of the European Chapter of The Association for Computational Linguistics: Volume 1

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While previous research on readability has typically focused on document-level measures, recent work in areas such as natural language generation has pointed out the need of sentence-level readability measures. Much of psycholinguistics has focused for many years on processing measures that provide difficulty estimates on a word-by-word basis. However, these psycholinguistic measures have not yet been tested on sentence readability ranking tasks. In this paper, we use four psycholinguistic measures: idea density, surprisal, integration cost, and embedding depth to test whether these features are predictive of readability levels. We find that psycholinguistic features significantly improve performance by up to 3 percentage points over a standard document-level readability metric baseline.

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Propositional Idea Density in aphasic discourse

Cited by 23 publications

References 25 publications

Propositional idea density in older men’s written language: Findings from the HIMS study using computerised analysis

Propositional idea density in older men’s written language: Findings from the HIMS study using computerised analysis

Lexical Features Are More Vulnerable, Syntactic Features Have More Predictive Power

Psycholinguistic Models of Sentence Processing Improve Sentence Readability Ranking

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