Quantifying Semantic Linguistic Maturity in Children

Hansson, Kristina; Bååth, Rasmus; Löhndorf, Simone; Sahlén, Birgitta; Sikström, Sverker

doi:10.1007/s10936-015-9398-7

Cited by 5 publications

(8 citation statements)

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“…Although a particular feature of bird in a feature list might be "has wings," the presence of which has birdlike meaning on its own, the meaning of bird in a semantic space model is the aggregate distributed pattern of all the abstract dimensions, none of which has interpretable meaning on its own." Hence, distributional vector dimensions are usually described as not meaningful or interpretable (e.g., Borghesani & Piazza, 2017;Hansson, Bååth, Löhndorf, Sahlén, & Sikström, 2016), rendering distributional vectors "devoid of content" (Rogers & Wolmetz, 2016, p. 124). This is an especially troublesome property for many linguistic theories of semantics, which often heavily rely on well-defined semantic features (see Bierwisch, 2011;Nida, 1979;Johnson, 2008), but many psychological theories of word meanings and concepts are also founded on interpretable features (e.g.…”

Section: Are Distributional Vector Dimensions Uninterpretable?mentioning

confidence: 99%

“…In DSMs as outlined so far, word vectors are constructed on the basis of surface-level word co-occurrences, without consideration for the relations or syntactic dependencies between the words (as discussed by Hansson et al, 2016). Although moving window models such as HAL or word2vec encode some syntax, this takes place on a very rudimentary level.…”

Section: Shark(lawyer) Inmentioning

confidence: 99%

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Vector-Space Models of Semantic Representation From a Cognitive Perspective: A Discussion of Common Misconceptions

Günther

Rinaldi

Marelli

2019

Perspect Psychol Sci

258

276

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Models that represent meaning as high-dimensional numerical vectors—such as latent semantic analysis (LSA), hyperspace analogue to language (HAL), bound encoding of the aggregate language environment (BEAGLE), topic models, global vectors (GloVe), and word2vec—have been introduced as extremely powerful machine-learning proxies for human semantic representations and have seen an explosive rise in popularity over the past 2 decades. However, despite their considerable advancements and spread in the cognitive sciences, one can observe problems associated with the adequate presentation and understanding of some of their features. Indeed, when these models are examined from a cognitive perspective, a number of unfounded arguments tend to appear in the psychological literature. In this article, we review the most common of these arguments and discuss (a) what exactly these models represent at the implementational level and their plausibility as a cognitive theory, (b) how they deal with various aspects of meaning such as polysemy or compositionality, and (c) how they relate to the debate on embodied and grounded cognition. We identify common misconceptions that arise as a result of incomplete descriptions, outdated arguments, and unclear distinctions between theory and implementation of the models. We clarify and amend these points to provide a theoretical basis for future research and discussions on vector models of semantic representation.

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Section: Are Distributional Vector Dimensions Uninterpretable?mentioning

confidence: 99%

Section: Shark(lawyer) Inmentioning

confidence: 99%

Vector-Space Models of Semantic Representation From a Cognitive Perspective: A Discussion of Common Misconceptions

Günther

Rinaldi

Marelli

2019

Perspect Psychol Sci

258

276

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“…Landauer (1999) maintains that LSA captures "a theory of the psychology of language and mind" by arguing that "it offers a biologically and psychologically plausible mechanistic explanation of the acquisition, induction, and representation of verbal meaning" (p. 303). Naturally, statistical semantics has been used for studying cognitive aspects, such as semantic linguistic maturity in children (Hansson, Bååth, Löhndorf, Sahlén, & Sikström, 2015), episodic memory recall (Howard & Kahana, 2002), correctness of eyewitness statements (Sarwar, Sikström, Allwood, & InnesKer, 2015) and word association in patients diagnosed with Broca's aphasia (Hansson et al, 2015). This is truly important, as it is an investigative tool directly related to understanding language and to further the understanding of meaning and knowledge representation.…”

Section: Applying Statistical Semantics In Psychological Researchmentioning

confidence: 99%

Conceptualizing and Measuring Well-Being Using Statistical Semantics and Numerical Rating Scales

Kjell¹

2018

Preprint

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How to define and measure individuals’ well-being is important, as this has an impact on both research and society at large. This thesis concerns how to define and measure the self-reported well-being of individuals, which involves both theorizing as well as developing and applying empirical and statistical methods in order to gain a better understanding of well-being.The first paper critically reviews the literature on well-being. It identifies an individualistic bias in current approaches and accompanying measures related to well-being and happiness; for example, through an over-emphasis on the importance of self-centered aspects of well-being (e.g., the unprecedented focus on satisfaction with life) whilst disregarding the importance of harmony in life, interconnectedness and psychological balance in relation to well- being. It is also discussed how closed-ended well-being measures impose the researchers’ values and limit the ability of respondents to express themselves in regard to their perceived well-being.The second paper addresses concerns regarding this individualistic bias by developing the harmony in life scale, which focuses on interconnectedness and psychological balance. In addition, an open-ended approach is developed in the paper, allowing individuals to freely describe their pursuit of well-being by means of open-ended responses analyzed using statistical semantics (including techniques from artificial intelligence such as natural language processing and machine learning). The results show that the harmony in life scale and the traditional satisfaction with life scale form a two-factor model of well-being, where the harmony in life scale explains more unique variance in measures of psychological well-being, stress, depression and anxiety, but not happiness. It is further demonstrated that participants describe their pursuit of harmony in life using words related to interconnectedness (including words such as: peace, balance, cooperation), whereas they describe their pursuit of satisfaction with life using words related to independence (including words such as: money, achievement, fulfillment). It is concluded that the harmony in life scale complements the satisfaction with life scale for a more comprehensive understanding of subjective well-being.The third paper focuses on developing and evaluating a method for measuring and describing psychological constructs using open-ended questions analyzed by means of statistical semantics rather than closed-ended numerical rating scales. This semantic measures approach is tested and compared with traditional rating scales in nine studies, including two different paradigms involving reports regarding objective stimuli (i.e., the evaluation of facial expressions) and reports regarding subjective states (i.e., the self-reporting of harmony in life, satisfaction with life, depression and worry). The results indicate that semantic measures encompass higher, or competitive, levels of reliability and validity compared to traditional numerical rating scales. In addition, semantic measures appear to be better suited for differentiating between psychological constructs, such as harmony in life versus satisfaction with life as well as depression versus worry.In this thesis, the findings from these three papers are elaborated and integrated into two independent perspectives. The first perspective focuses on the theoretical and empirical differences between harmony in life and satisfaction with life within a context of societal and national progress. It is concluded that harmony in life complements satisfaction with life. The second perspective focuses on the open-ended, statistical semantics approach. It is proposed that statistical semantics may beneficially be used more widely as a research tool within psychological research.

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“…Furthermore, earlier studies have not focused on objective methods to quantify semantic content at text the level, possibly due to lack of appropriate methods to measure this. In a recent study, Hansson et al (2016) introduced a method, based on Latent Semantic Analysis (LSA, Landauer and Dumais 1997), which provides opportunities to quantify semantic linguistic maturity in children’s narratives with the aim of providing insights into semantic development. This method first generates a high dimensional semantic representation of words that are created from the co-occurrence of words in a large text corpus.…”

Section: Introductionmentioning

confidence: 99%

“…In a recent study (Hansson et al 2016), LSA was used on narratives from Swedish-speaking children to generate a measure of semantic linguistic maturity (SELMA) based on a semantic representation of words. The semantic representation was created from the co-occurrence of words in a large text corpus on spoken narratives from children with typical language development in the age range 4–11.…”

Section: Introductionmentioning

confidence: 99%

Latent Semantic Analysis Discriminates Children with Developmental Language Disorder (DLD) from Children with Typical Language Development

Bååth

Sikström

Kalnak

et al. 2019

J Psycholinguist Res

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Computer based analyses offer a possibility for objective methods to assess semantic-linguistic quality of narratives at the text level. The aim of the present study is to investigate whether a semantic language impairment index (SELIMI) based on latent semantic analysis (LSA) can discriminate between children with developmental language disorder (DLD) and children with typical language development. Spoken narratives from 54 children with DLD and 54 age matched controls with typical language development were summarized in a semantic representation generated using LSA. A statistical model was trained to discriminate between children with DLD and children with typical language development, given the semantic vector representing each individual child’s narrative. The results show that SELIMI could distinguish between children with DLD and children with typical language development significantly better than chance and thus has a potential to complement traditional analyses focussed on form or on the word level.

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Quantifying Semantic Linguistic Maturity in Children

Cited by 5 publications

References 50 publications

Vector-Space Models of Semantic Representation From a Cognitive Perspective: A Discussion of Common Misconceptions

Vector-Space Models of Semantic Representation From a Cognitive Perspective: A Discussion of Common Misconceptions

Conceptualizing and Measuring Well-Being Using Statistical Semantics and Numerical Rating Scales

Latent Semantic Analysis Discriminates Children with Developmental Language Disorder (DLD) from Children with Typical Language Development

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