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Titlebook: Computational Linguistics and Intelligent Text Processing; 19th International C Alexander Gelbukh Conference proceedings 2023 Springer Natu

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發(fā)表于 2025-3-23 12:09:03 | 只看該作者
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Self-adaptive Privacy Concern Detection for?User-Generated Contentysis output. The noise masks individual’s sensitive information contained in the dataset. However, determining the amount of noise is a key challenge, since too much noise will destroy data utility while too little noise will increase privacy risk. Though previous research works have designed some m
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發(fā)表于 2025-3-24 04:56:46 | 只看該作者
Complexity of?Russian Academic Texts as?the?Function of?Syntactic Parametersrs, therefore automation of text complexity assessment is relevant in a number of areas. The authors propose an innovative approach addressing limitations of the existing Russian text complexity tools based on descriptive and lexical features only. We examined the impact of 75 syntactic features on
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An Experimental Approach for?Information Extraction in?Multi-party Dialogue Discourseg formatting and punctuations. In addition, the information are distributed over multiple sentences. We experimentally investigate the usefulness of numerical statistics and topic modelling methods on a real dataset containing multi-part dialogue texts. Such information extraction can be used for di
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https://doi.org/10.1007/978-3-031-23793-5artificial intelligence; computational linguistics; computer science; computer systems; correlation anal
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