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Tweedie, Fiona J.; Baayen, R. Harald

doi:10.1023/a:1001749303137

Cited by 271 publications

(43 citation statements)

References 21 publications

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“…Although the means of the type-token ratios were in the right direction, with more repetition (lower type-token ratio) in the AD group than in the control group, the differences were not significant, possibly due to sample size or due to within-group variance. We note that type-token ratio is highly affected by the total number of words (e.g., Durán, Malvern, Richards, & Chipere, 2004; Tweedie & Baayen, 1998). While there was no inter-group difference in the total number of words produced, there was intra-group variance in this measure, especially within the AD group, which could have affected the TTR analysis.…”

Section: Discussionmentioning

confidence: 93%

Word retrieval in picture descriptions produced by individuals with Alzheimer’s disease

Kavé

Goral

2016

Journal of Clinical and Experimental Neuropsychology

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What can tests of single-word production tell us about word retrieval in connected speech? We examined this question in 20 people with Alzheimer's disease (AD) and in 20 cognitively intact individuals. All participants completed tasks of picture naming and semantic fluency, and provided connected speech through picture descriptions. Picture descriptions were analyzed for total word output, percentages of content words, percentages of nouns, and percentages of pronouns out of all words, type-token ratio of all words and type-token ratio of nouns alone, mean frequency of all words and mean frequency of nouns alone, and mean word length. Individuals with AD performed worse than did cognitively intact individuals on the picture naming and semantic fluency tasks. They also produced a lower proportion of content words overall, a lower proportion of nouns, and a higher proportion of pronouns, as well as more frequent and shorter words on picture descriptions. Group differences in total word output and type-token ratios did not reach significance. Correlations between scores on tasks of single-word retrieval and measures of retrieval in picture descriptions emerged in the AD group but not in the control group. Scores on a picture naming task were associated with difficulties in word retrieval in connected speech in AD, while scores on a task of semantic verbal fluency were less useful in predicting measures of retrieval in context in this population.

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Section: Discussionmentioning

confidence: 93%

Word retrieval in picture descriptions produced by individuals with Alzheimer’s disease

Kavé

Goral

2016

Journal of Clinical and Experimental Neuropsychology

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“…The literature suggests a broad amount of stylometric features (cf. Mosteller and Wallace 1964; Tweedie and Baayen 1998 or Stamatatos 2009). We filter the content features and some of those who do not make sense in short texts.…”

Section: Resultsmentioning

confidence: 99%

Authorship identification of documents with high content similarity

et al. 2018

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The goal of our work is inspired by the task of associating segments of text to their real authors. In this work, we focus on analyzing the way humans judge different writing styles. This analysis can help to better understand this process and to thus simulate/ mimic such behavior accordingly. Unlike the majority of the work done in this field (i.e. authorship attribution, plagiarism detection, etc.) which uses content features, we focus only on the stylometric, i.e. content-agnostic, characteristics of authors. Therefore, we conducted two pilot studies to determine, if humans can identify authorship among documents with high content similarity. The first was a quantitative experiment involving crowd-sourcing, while the second was a qualitative one executed by the authors of this paper. Both studies confirmed that this task is quite challenging. To gain a better understanding of how humans tackle such a problem, we conducted an exploratory data analysis on the results of the studies. In the first experiment, we compared the decisions against content features and stylometric features. While in the second, the evaluators described the process and the features on which their judgment was based. The findings of our detailed analysis could (1) help to improve algorithms such as automatic authorship attribution as well as plagiarism detection, (2) assist forensic experts or linguists to create profiles of writers, (3) support intelligence applications to analyze aggressive and threatening messages and (4) help editor conformity by adhering to, for instance, journal specific writing style.

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“…Let S t denote the event that a section s [ {s 1 , …, s n } belongs to the target group (= not plagiarized); likewise, let S o denote the event that s belongs to the outlier Character n-gram frequency/ratio* Kjell et al (1994), Sanderson and Guenter (2006a), Juola (2006) and Koppel (2009) Average sentence length Holmes (1998) and Zheng et al (2006) Average number of syllables per word* Holmes (1998) Word frequency Mosteller and Wallace (1964), Holmes (1998) and Koppel (2009) Word n-grams frequency/ratio Sanderson and Guenter (2006a) Number of hapax legomena Tweedie and Baayen (1998) and Zheng et al…”

Section: Outlier Identificationmentioning

confidence: 99%

“…Dale-Chall index Dale and Chall (1948) and Chall and Dale (1995) Flesch Kincaid grade level* Flesch (1948) and Kincaid et al (1975) Gunning Fog index* Gunning (1952) Honore's R measure* Honore (1979), Tweedie and Baayen (1998) and Zheng et al (2006) Sichel's S measure Tweedie and Baayen (1998) and Zheng et al…”

Section: Outlier Identificationmentioning

confidence: 99%

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Intrinsic plagiarism analysis

Stein

Lipka

Prettenhofer

2010

Lang Resources & Evaluation

112

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Research in automatic text plagiarism detection focuses on algorithms that compare suspicious documents against a collection of reference documents. Recent approaches perform well in identifying copied or modified foreign sections, but they assume a closed world where a reference collection is given. This article investigates the question whether plagiarism can be detected by a computer program if no reference can be provided, e.g., if the foreign sections stem from a book that is not available in digital form. We call this problem class intrinsic plagiarism analysis; it is closely related to the problem of authorship verification. Our contributions are threefold. (1) We organize the algorithmic building blocks for intrinsic plagiarism analysis and authorship verification and survey the state of the art.(2) We show how the meta learning approach of Koppel and Schler, termed ''unmasking'', can be employed to post-process unreliable stylometric analysis results. (3) We operationalize and evaluate an analysis chain that combines document chunking, style model computation, one-class classification, and meta learning. Problem statementIn the following, the term plagiarism refers to text plagiarism, i.e., the use of another author's information, language, or writing, when done without proper acknowledgment of the original source. Plagiarism detection refers to the unveiling of text plagiarism. Existing approaches to computer-based plagiarism detection break down this task into manageable parts:''Given a text d and a reference collection D, does d contain a section s for which one can find a document d i [ D that contains a section s i such that under some retrieval model R the similarity u R between s and s i is above a threshold h?''Observe that research on automated plagiarism detection presumes a closed world where a reference collection D is given. Since D can be extremely largepossibly the entire indexed part of the World Wide Web-the main research focus is on efficient search technology: near-similarity search and near-duplicate detection (Brin et al

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Cited by 271 publications

References 21 publications

Word retrieval in picture descriptions produced by individuals with Alzheimer’s disease

Word retrieval in picture descriptions produced by individuals with Alzheimer’s disease

Authorship identification of documents with high content similarity

Intrinsic plagiarism analysis

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