The Stylometric Impacts of Ageing and Life Events on Identity

Kernot, David; Bossomaier, Terry; Bradbury, Roger

doi:10.1080/09296174.2017.1405719

Cited by 12 publications

(10 citation statements)

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“…Perceptual strength norms (also termed modality exclusivity norms, after the original Lynott & Connell, 2009, work) now exist in several different languages, including Russian (Miklashevsky, 2018), Serbian (Filipović Đurđević, Popović Stijačić, & Karapandžić, 2016, Dutch (Speed & Majid, 2017), and Mandarin (Chen, Zhao, Long, Lu, & Huang, 2019), and have been developed for concept-property pairs as well as individual words (van Dantzig, Cowell, Zeelenberg, & Pecher, 2011). The original English-language norms have also been applied in novel ways, such as examining stylistic differences of authors (Kernot, Bossomaier, & Bradbury, 2019), studying perceptual metaphors (e.g., rough sound, smooth melody; Winter, 2019), testing models of lexical representations (Johns & Jones, 2012), evaluating the iconicity of words in written (Winter, Perlman, Perry, & Lupyan, 2017), and signed languages (Perlman, Little, Thompson, & Thompson, 2018), and discovering links between sensory and emotional experience (Winter, 2016).…”

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confidence: 99%

The Lancaster Sensorimotor Norms: multidimensional measures of perceptual and action strength for 40,000 English words

et al. 2019

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Sensorimotor information plays a fundamental role in cognition. However, the existing materials that measure the sensorimotor basis of word meanings and concepts have been restricted in terms of their sample size and breadth of sensorimotor experience.Here we present norms of sensorimotor strength for 39,707 concepts across six perceptual modalities (touch, hearing, smell, taste, vision, and interoception) and five action effectors (mouth/throat, hand/arm, foot/leg, head excluding mouth/throat, and torso), gathered from a total of 3,500 individual participants using Amazon's Mechanical Turk platform. The Lancaster Sensorimotor Norms are unique and innovative in a number of respects: They represent the largest-ever set of semantic norms for English, at 40,000 words × 11 dimensions (plus several informative cross-dimensional variables), they extend perceptual strength norming to the new modality of interoception, and they include the first norming of action strength across separate bodily effectors. In the first study, we describe the data collection procedures, provide summary descriptives of the dataset, and interpret the relations observed between sensorimotor dimensions. We then report two further studies, in which we (1) extracted an optimal singlevariable composite of the 11-dimension sensorimotor profile (Minkowski 3 strength) and (2) demonstrated the utility of both perceptual and action strength in facilitating lexical decision times and accuracy in two separate datasets. These norms provide a valuable resource to researchers in diverse areas, including psycholinguistics, grounded cognition, cognitive semantics, knowledge representation, machine learning, and big-data approaches to the analysis of language and conceptual representations. The data are accessible via the Open Science Framework (http://osf.io/7emr6/) and an interactive web application (https://www. lancaster.ac.uk/psychology/lsnorms/).

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confidence: 99%

The Lancaster Sensorimotor Norms: multidimensional measures of perceptual and action strength for 40,000 English words

et al. 2019

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“…Russian (Miklashevsky, 2018), Serbian (Filipović Đurđević et al, 2016), Dutch (Speed & Majid, 2017), and Mandarin (Chen et al, 2019), and have been developed for conceptproperty pairs as well as individual words (van Dantzig, Cowell, Zeelenberg, & Pecher, 2011). The original English-language norms have also been applied in novel ways, such as examining stylistic differences of authors (Kernot, Bossomaier & Bradbury, 2019), studying perceptual metaphors (e.g., rough sound, smooth melody; Winter, 2019), testing models of lexical representations (Johns & Jones, 2012), evaluating the iconicity of words in written (Winter, Perlman, Perry & Lupyan, 2017) and signed languages (Perlman, Little, Thompson, & Thompson, 2018), and discovering links between sensory and emotional experience (Winter, 2016).…”

mentioning

confidence: 99%

The Lancaster Sensorimotor Norms: Multidimensional measures of Perceptual and Action Strength for 40,000 English words

Lynott¹,

Connell²,

Brysbaert³

et al. 2019

Preprint

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Sensorimotor information plays a fundamental role in cognition. However, existing materials that measure the sensorimotor basis to word meanings and concepts have been restricted in sample size and breadth of sensorimotor experience. Here, we present norms of sensorimotor strength for 39,707 concepts across six perceptual modalities (touch, hearing, smell, taste, vision, and interoception) and five action effectors (mouth/throat, hand/arm, foot/leg, head excluding mouth/throat, and torso), gathered from a total of 3,500 individual participants using Amazon's Mechanical Turk platform. The Lancaster Sensorimotor Norms are unique and innovative in a number of respects: they represent the largest ever set of semantic norms for English at 40 thousand words x 11 dimensions (plus several informative cross-dimensional variables); they extend perceptual strength norming to the new modality of interoception; and they include the first norming of action strength across separate bodily effectors. In the first study, we describe the data collection procedures, provide summary descriptives of the dataset, and interpret the relations observed between sensorimotor dimensions. We then report two further studies that i) extract an optimal single-variable composite of the 11-dimension sensorimotor profile (Minkowski 3 strength), and ii) demonstrate the utility of both perceptual and action strength in facilitating lexical decision times and accuracy in two separate datasets. These norms provide a valuable resource to researchers in diverse areas including psycholinguistics, grounded cognition, cognitive semantics, knowledge representation, machine learning, and big data approaches to the analysis of language and conceptual representations. The data are accessible via the Open Science Framework (http://osf.io/7emr6/ ) and an interactive web application (https://www.lancaster.ac.uk/psychology/lsnorms/).

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“…On the other hand, if the parameter dir = deptree, the example text in Figure 14 is represented in the following row matrix in (2). Note that each row represents a path from the root node towards the numerous leaf nodes as we walk down the dependency tree that is depicted in the Then, similar to the dir = spacial case, the sliding window, as depicted in Figure 1, will operate on the matrix in (2) on a row-by-row basis.…”

Section: B Generalization Of N -Gram Methodsmentioning

confidence: 99%

“…Electronic text (e-text) stylometry is concerned about analyzing the writing styles of input e-texts in order to extract information about their authors. Such inferred information could be the identity of the authors, their genders, age groups, personality types, or even the diagnosis of certain illnesses [1], [2]. Author Attribution (AA) is an important problem in e-text stylometry and is defined as follows: given a set of texts with known authors, find a classification model that predicts which of these known authors is also the author of the input test texts whose authors are not known.…”

Section: Introductionmentioning

confidence: 99%

“…., x i,d[8] = nmod, where det, amod, nsubj and nmod are dependency relations. Application-specific patterns: input texts are considered as arrays of application-specific patterns2 A list of function words in the English language can be found here: http: //www.sequencepublishing.com/academic.html3 http://nlp.stanford.edu:8080/parser/index.jsp 4 https://www.cis.upenn.edu/ ∼ treebank/…”

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Evaluating Author Attribution on Emirati Tweets

Khonji

Iraqi

2020

IEEE Access

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Author Attribution (AA) is a critical stylometry problem that tries to deduce the identity of the authors of electronic texts (e-texts) by only examining the texts. AA is essential for enhancing various application domains, such as recommender systems and forensics. Nevertheless, existing techniques in AA have not been assessed with Emirati social media e-texts. The reason is that no suitable dataset exists for evaluating AA techniques in this context. This paper introduces the Khonji-Iraqi Emirati Tweets Author Identification (AID) dataset with 30 authors (KIT-30), and detailed evaluations. Compound grams, a new definition of grams, are introduced, which allows us to achieve higher classification accuracy. Also, when the number of suspect authors increases, the classification accuracy degradation is not as severe as previously reported, when using suitable data representation. Furthermore, in order to work towards addressing the lack of conveniently-available implementations of stylometry methods, we have developed an extensive e-text feature extraction library, namely Fextractor, with a highly intuitive API. The library generalizes all existing n-gram-based feature extraction methods under the at least l-frequent, dir-directed, k-skipped n-grams, and allows grams to be diversely defined, including definitions that are based on high-level grammatical aspects, such as Part of Speech (POS) tags, as well as lower-level ones, such as the distribution of function words and word shapes.

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The Stylometric Impacts of Ageing and Life Events on Identity

Cited by 12 publications

References 62 publications

The Lancaster Sensorimotor Norms: multidimensional measures of perceptual and action strength for 40,000 English words

The Lancaster Sensorimotor Norms: multidimensional measures of perceptual and action strength for 40,000 English words

The Lancaster Sensorimotor Norms: Multidimensional measures of Perceptual and Action Strength for 40,000 English words

Evaluating Author Attribution on Emirati Tweets

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