The Experts below are selected from a list of 2484 Experts worldwide ranked by ideXlab platform
Stella Neumann - One of the best experts on this subject based on the ideXlab platform.
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CL4LC@COLING 2016 - CoCoGen – Complexity Contour Generator: Automatic Assessment of Linguistic Complexity Using a Sliding-Window Technique
2020Co-Authors: Marcus Strobel, Elma Kerz, Daniel Wiechmann, Stella NeumannAbstract:We present a novel approach to the automatic assessment of text Complexity based on a sliding-window technique that tracks the distribution of Complexity within a text. Such distribution is captured by what we term “Complexity contours” derived from a series of measurements for a given Linguistic Complexity measure. This approach is implemented in an automatic computational tool, CoCoGen – Complexity Contour Generator, which in its current version supports 32 indices of Linguistic Complexity. The goal of the paper is twofold: (1) to introduce the design of our computational tool based on a sliding-window technique and (2) to showcase this approach in the area of second language (L2) learning, i.e. more specifically, in the area of L2 writing.
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cocogen Complexity contour generator automatic assessment of Linguistic Complexity using a sliding window technique
International Conference on Computational Linguistics, 2016Co-Authors: Marcus Strobel, Elma Kerz, Daniel Wiechmann, Stella NeumannAbstract:We present a novel approach to the automatic assessment of text Complexity based on a sliding-window technique that tracks the distribution of Complexity within a text. Such distribution is captured by what we term “Complexity contours” derived from a series of measurements for a given Linguistic Complexity measure. This approach is implemented in an automatic computational tool, CoCoGen – Complexity Contour Generator, which in its current version supports 32 indices of Linguistic Complexity. The goal of the paper is twofold: (1) to introduce the design of our computational tool based on a sliding-window technique and (2) to showcase this approach in the area of second language (L2) learning, i.e. more specifically, in the area of L2 writing.
Marcus Strobel - One of the best experts on this subject based on the ideXlab platform.
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CL4LC@COLING 2016 - CoCoGen – Complexity Contour Generator: Automatic Assessment of Linguistic Complexity Using a Sliding-Window Technique
2020Co-Authors: Marcus Strobel, Elma Kerz, Daniel Wiechmann, Stella NeumannAbstract:We present a novel approach to the automatic assessment of text Complexity based on a sliding-window technique that tracks the distribution of Complexity within a text. Such distribution is captured by what we term “Complexity contours” derived from a series of measurements for a given Linguistic Complexity measure. This approach is implemented in an automatic computational tool, CoCoGen – Complexity Contour Generator, which in its current version supports 32 indices of Linguistic Complexity. The goal of the paper is twofold: (1) to introduce the design of our computational tool based on a sliding-window technique and (2) to showcase this approach in the area of second language (L2) learning, i.e. more specifically, in the area of L2 writing.
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cocogen Complexity contour generator automatic assessment of Linguistic Complexity using a sliding window technique
International Conference on Computational Linguistics, 2016Co-Authors: Marcus Strobel, Elma Kerz, Daniel Wiechmann, Stella NeumannAbstract:We present a novel approach to the automatic assessment of text Complexity based on a sliding-window technique that tracks the distribution of Complexity within a text. Such distribution is captured by what we term “Complexity contours” derived from a series of measurements for a given Linguistic Complexity measure. This approach is implemented in an automatic computational tool, CoCoGen – Complexity Contour Generator, which in its current version supports 32 indices of Linguistic Complexity. The goal of the paper is twofold: (1) to introduce the design of our computational tool based on a sliding-window technique and (2) to showcase this approach in the area of second language (L2) learning, i.e. more specifically, in the area of L2 writing.
Elma Kerz - One of the best experts on this subject based on the ideXlab platform.
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CL4LC@COLING 2016 - CoCoGen – Complexity Contour Generator: Automatic Assessment of Linguistic Complexity Using a Sliding-Window Technique
2020Co-Authors: Marcus Strobel, Elma Kerz, Daniel Wiechmann, Stella NeumannAbstract:We present a novel approach to the automatic assessment of text Complexity based on a sliding-window technique that tracks the distribution of Complexity within a text. Such distribution is captured by what we term “Complexity contours” derived from a series of measurements for a given Linguistic Complexity measure. This approach is implemented in an automatic computational tool, CoCoGen – Complexity Contour Generator, which in its current version supports 32 indices of Linguistic Complexity. The goal of the paper is twofold: (1) to introduce the design of our computational tool based on a sliding-window technique and (2) to showcase this approach in the area of second language (L2) learning, i.e. more specifically, in the area of L2 writing.
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cocogen Complexity contour generator automatic assessment of Linguistic Complexity using a sliding window technique
International Conference on Computational Linguistics, 2016Co-Authors: Marcus Strobel, Elma Kerz, Daniel Wiechmann, Stella NeumannAbstract:We present a novel approach to the automatic assessment of text Complexity based on a sliding-window technique that tracks the distribution of Complexity within a text. Such distribution is captured by what we term “Complexity contours” derived from a series of measurements for a given Linguistic Complexity measure. This approach is implemented in an automatic computational tool, CoCoGen – Complexity Contour Generator, which in its current version supports 32 indices of Linguistic Complexity. The goal of the paper is twofold: (1) to introduce the design of our computational tool based on a sliding-window technique and (2) to showcase this approach in the area of second language (L2) learning, i.e. more specifically, in the area of L2 writing.
Daniel Wiechmann - One of the best experts on this subject based on the ideXlab platform.
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CL4LC@COLING 2016 - CoCoGen – Complexity Contour Generator: Automatic Assessment of Linguistic Complexity Using a Sliding-Window Technique
2020Co-Authors: Marcus Strobel, Elma Kerz, Daniel Wiechmann, Stella NeumannAbstract:We present a novel approach to the automatic assessment of text Complexity based on a sliding-window technique that tracks the distribution of Complexity within a text. Such distribution is captured by what we term “Complexity contours” derived from a series of measurements for a given Linguistic Complexity measure. This approach is implemented in an automatic computational tool, CoCoGen – Complexity Contour Generator, which in its current version supports 32 indices of Linguistic Complexity. The goal of the paper is twofold: (1) to introduce the design of our computational tool based on a sliding-window technique and (2) to showcase this approach in the area of second language (L2) learning, i.e. more specifically, in the area of L2 writing.
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cocogen Complexity contour generator automatic assessment of Linguistic Complexity using a sliding window technique
International Conference on Computational Linguistics, 2016Co-Authors: Marcus Strobel, Elma Kerz, Daniel Wiechmann, Stella NeumannAbstract:We present a novel approach to the automatic assessment of text Complexity based on a sliding-window technique that tracks the distribution of Complexity within a text. Such distribution is captured by what we term “Complexity contours” derived from a series of measurements for a given Linguistic Complexity measure. This approach is implemented in an automatic computational tool, CoCoGen – Complexity Contour Generator, which in its current version supports 32 indices of Linguistic Complexity. The goal of the paper is twofold: (1) to introduce the design of our computational tool based on a sliding-window technique and (2) to showcase this approach in the area of second language (L2) learning, i.e. more specifically, in the area of L2 writing.
Koenraad S. Rhebergen - One of the best experts on this subject based on the ideXlab platform.
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Linguistic Complexity of Speech Recognition Test Sentences and Its Influence on Children's Verbal Repetition Accuracy.
Ear and Hearing, 2020Co-Authors: Hanneke E. M. Van Der Hoek-snieders, Inge Stegeman, Adriana L. Smit, Koenraad S. RhebergenAbstract:OBJECTIVES: Speech recognition (SR)-tests have been developed for children without considering the Linguistic Complexity of the sentences used. However, Linguistic Complexity is hypothesized to influence correct sentence repetition. The aim of this study is to identify lexical and grammatical parameters influencing verbal repetition accuracy of sentences derived from a Dutch SR-test when performed by 6-year-old typically developing children. DESIGN: For this observational, cross-sectional study, 40 typically developing children aged 6 were recruited at four primary schools in the Netherlands. All children performed a sentence repetition task derived from an SR-test for adults. The sentence Complexity was described beforehand with one lexical parameter, age of acquisition, and four grammatical parameters, specifically sentence length, prepositions, sentence structure, and verb inflection. A multiple logistic regression analysis was performed. RESULTS: Sentences with a higher age of acquisition (odds ratio [OR] = 1.59) or greater sentence length (OR = 1.28) had a higher risk of repetition inaccuracy. Sentences including a spatial (OR = 1.25) or other preposition (OR = 1.25) were at increased risk for incorrect repetition, as were complex sentences (OR = 1.69) and sentences in the present perfect (OR = 1.44) or future tense (OR = 2.32). CONCLUSIONS: The variation in verbal repetition accuracy in 6-year-old children is significantly influenced by both lexical and grammatical parameters. Linguistic Complexity is an important factor to take into account when assessing speech intelligibility in children.
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Linguistic Complexity of speech recognition test sentences and its influence on children s verbal repetition accuracy
Ear and Hearing, 2020Co-Authors: Hanneke E M Van Der Hoeksnieders, Inge Stegeman, Adriana L. Smit, Koenraad S. RhebergenAbstract:Objectives Speech recognition (SR)-tests have been developed for children without considering the Linguistic Complexity of the sentences used. However, Linguistic Complexity is hypothesized to influence correct sentence repetition. The aim of this study is to identify lexical and grammatical parameters influencing verbal repetition accuracy of sentences derived from a Dutch SR-test when performed by 6-year-old typically developing children. Design For this observational, cross-sectional study, 40 typically developing children aged 6 were recruited at four primary schools in the Netherlands. All children performed a sentence repetition task derived from an SR-test for adults. The sentence Complexity was described beforehand with one lexical parameter, age of acquisition, and four grammatical parameters, specifically sentence length, prepositions, sentence structure, and verb inflection. A multiple logistic regression analysis was performed. Results Sentences with a higher age of acquisition (odds ratio [OR] = 1.59) or greater sentence length (OR = 1.28) had a higher risk of repetition inaccuracy. Sentences including a spatial (OR = 1.25) or other preposition (OR = 1.25) were at increased risk for incorrect repetition, as were complex sentences (OR = 1.69) and sentences in the present perfect (OR = 1.44) or future tense (OR = 2.32). Conclusions The variation in verbal repetition accuracy in 6-year-old children is significantly influenced by both lexical and grammatical parameters. Linguistic Complexity is an important factor to take into account when assessing speech intelligibility in children.