Age Effects on Semantic Coherence: Latent Semantic Analysis Applied to Letter Fluency Data

Marklund, Petter; Sikström, Sverker; Bååth, Rasmus; Nilsson, Lars‐Göran

doi:10.1109/semapro.2009.16

Cited by 3 publications

(5 citation statements)

References 11 publications

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“…Further semantic analyses were then conducted using the Semanticexcel software, which is a web-based software for statistical analyses of semantic representations, that has been written by the last author of this paper (www.semanticexcel.com) and has been used in other studies (Andersson et al, 2012;Arvidsson et al, 2011;Garcia & Sikström, 2012;Garcia & Sikström, 2014;Gustafsson, Sikström, et al, in press;Karlsson et al, 2013;Kjell et al, 2013;Marklund et al, 2009;Roll et al, 2011;Rosenberg et al, 2013;Sarwar et al, in press; Willander & Sikström, in press). The narratives generated by the subjects were summarized in the semantic representation generated by LSA.…”

Section: Discussionmentioning

confidence: 99%

“…Our research group has developed methods for the creation of semantic spaces and applied them in different contexts. For details of these methods see for example (Andersson, Bååth, & Sikström, 2012;Arvidsson et al, 2011;Garcia & Sikström, 2012;Garcia & Sikström, 2014;Gustafsson, Sikström, & Lindholm, in press;Karlsson et al, 2013;Kjell, Nima, Sikström, Archer, & Garcia, 2013;Marklund, Sikström, Bååth, & Nilsson, 2009;Roll et al, 2011;Rosenberg, Sikström, & Garcia, 2013;Sarwar, Sikström, Allwood, & Kerr-Innes, in press;Willander & Sikström, in press). Here we summarize some of the main properties of semantic space methods, and for methodological details we refer the interested reader to references above.…”

Section: Semantic Spacesmentioning

confidence: 99%

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Quantifying Semantic Linguistic Maturity in Children

Hansson

Bååth

Löhndorf

et al. 2015

J Psycholinguist Res

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We propose a method to quantify semantic linguistic maturity (SELMA) based on a high dimensional semantic representation of words created from the co-occurrence of words in a large text corpus. The SELMA method was applied to oral narratives from 108 children aged 4;0-12;10. By comparing the SELMA measure with maturity ratings made by human raters we found that SELMA predicted the rating of semantic maturity made by human raters over and above the prediction made using a child's age and number of words produced. We conclude that the semantic content of narratives changes in a predictable pattern with children's age and argue that SELMA is a measure quantifying semantic linguistic maturity. The study opens up the possibility of using quantitative measures for studying the development of semantic representation in children's narratives, and emphasizes the importance of word co-occurrences for understanding the development of meaning.

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Section: Discussionmentioning

confidence: 99%

Section: Semantic Spacesmentioning

confidence: 99%

Quantifying Semantic Linguistic Maturity in Children

Hansson

Bååth

Löhndorf

et al. 2015

J Psycholinguist Res

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“…All but one task (Letter A) were part of the "Regensburger Wortflüssigkeitstest" (RWT) [3], a standardized and norm-referenced German test of word generation ability. The task Letter A was used following the paper of Marklund et al [27].…”

Section: Word Generation Tasksmentioning

confidence: 99%

“…In addition, our analysis focuses not only on features provided by the test, but includes additional measures. A semantic measure employed in our study -semantic coherence (calculated as the mean of the semantic distances of consecutive responses) -has previously been applied to word generation tasks by Marklund et al [27]. The authors investigated the semantic coherence in two word generation tasks of varying complexities in people of different ages.…”

Section: Introductionmentioning

confidence: 99%

An evaluation of measures to dissociate language and communication disorders from healthy controls using machine learning techniques

Gaspers

Thiele

Cimiano

et al. 2012

Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium

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Reliably distinguishing patients with verbal impairment due to brain damage, e.g. aphasia, cognitive communication disorder (CCD), from healthy subjects is an important challenge in clinical practice. A widely-used method is the application of word generation tasks, using the number of correct responses as a performance measure. Though clinically well-established, its analytical and explanatory power is limited. In this paper, we explore whether additional features extracted from task performance can be used to distinguish healthy subjects from aphasics or CCD patients. We considered temporal, lexical, and sublexical features and used machine learning techniques to obtain a model that minimizes the empirical risk of classifying participants incorrectly. Depending on the type of word generation task considered, the exploitation of features with state-of-the-art machine learning techniques outperformed the predictive accuracy of the clinical standard method (number of correct responses). Our analyses confirmed that number of correct responses is an adequate measure for distinguishing aphasics from healthy subjects. However, our additional features outperformed the traditional clinical measure in distinguishing patients with CCD from healthy subjects: The best classification performance was achieved by excluding number of correct * The first two authors contributed equally to this work.

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“…Importantly, LSA overcame certain issues of the classical vector-space model. While earlier projects applied LSA to the general analysis of verbal-fluency data (e.g., Elvevåg et al, 2010;Marklund et al, 2009), Ryan (2013) pioneered the use of LSA techniques in automatic clustering analysis. This author built a semantic space for the animals domain by subjecting approximately 5,000 Wikipedia articles about animals to LSA.…”

Section: Automatic Analysis Of Lexical Organizationmentioning

confidence: 99%

The use of lexical retrieval strategies by creative second language learners: A computational analysis of clustering and switching

Fernández-Fontecha,

Ryan

2023

10.14746/ssllt

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This study seeks to delve into the potential role of divergent thinking, a component of creativity, in second language learning. Specifically, we compare the use of lexical organization and production strategies of two groups of more and less creative EFL learners in year 12 through an automatic vectorial semantic analysis of their retrieval in three second language semantic fluency tasks. Consistent with previous research in the field of creativity, our findings indicate that the creative group retrieved more second language words than the less creative group. These words were less related to each other and to the stimulus categories than the words generated by the less creative group. While the creative participants’ retrieval was based on an extensive use of switching, a slight but non-significant trend was found in the production of longer clusters by the less creative participants. These results yield interesting insights into the potential role of creativity in second language learning.

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Age Effects on Semantic Coherence: Latent Semantic Analysis Applied to Letter Fluency Data

Cited by 3 publications

References 11 publications

Quantifying Semantic Linguistic Maturity in Children

Quantifying Semantic Linguistic Maturity in Children

An evaluation of measures to dissociate language and communication disorders from healthy controls using machine learning techniques

The use of lexical retrieval strategies by creative second language learners: A computational analysis of clustering and switching

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