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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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樓主: 是英寸
41#
發(fā)表于 2025-3-28 14:39:14 | 只看該作者
Detection of Suicidal Intentions of Tunisians via Facebooke set of the most relevant attributes in identification of suicidal ideation especially among Tunisians. We reached encouraging results after a number of classification experiments thanks to an empirical comparison of the obtained performances by several subsets of features.
42#
發(fā)表于 2025-3-28 22:48:18 | 只看該作者
43#
發(fā)表于 2025-3-29 01:45:33 | 只看該作者
Sentiment Analysis of?Code-Mixed Languages Leveraging Resource Rich Languageses to a common sentiment space. Also, we introduce a basic clustering based preprocessing method to capture variations of code-mixed transliterated words. Our experiments reveal that SACMT outperforms the state-of-the-art approaches in sentiment analysis for code-mixed text by 7.6% in accuracy and 10.1% in F-score.
44#
發(fā)表于 2025-3-29 03:41:54 | 只看該作者
Cross-Framework Evaluation for?Portuguese POS Taggers and?Parsersrase identification, for extrinsic evaluation. The comparison proposed in this work takes into account the different linguistic theories and frameworks each parser subscribes to, but it is not dependent of any particular one.
45#
發(fā)表于 2025-3-29 09:40:39 | 只看該作者
46#
發(fā)表于 2025-3-29 14:00:42 | 只看該作者
https://doi.org/10.1007/978-3-663-04532-8 method to compress embedding matrices and reduce both the size and computation of the models. We conduct experiments on the PTB dataset and also test its performance on cellphones to illustrate its effectiveness.
47#
發(fā)表于 2025-3-29 17:15:37 | 只看該作者
https://doi.org/10.1007/978-3-642-94859-6es to a common sentiment space. Also, we introduce a basic clustering based preprocessing method to capture variations of code-mixed transliterated words. Our experiments reveal that SACMT outperforms the state-of-the-art approaches in sentiment analysis for code-mixed text by 7.6% in accuracy and 10.1% in F-score.
48#
發(fā)表于 2025-3-29 19:46:56 | 只看該作者
49#
發(fā)表于 2025-3-30 00:17:30 | 只看該作者
50#
發(fā)表于 2025-3-30 04:36:04 | 只看該作者
Using Shallow Semantic Analysis to?Implement Automated Quality Assessment of?Web Health Care Informanformation on the web. In many previous studies, human raters check the concordance between text content and evidence-based practice guidelines in order to evaluate information accuracy and completeness. However, human rating cannot be a practical solution, particularly when there is an extremely la
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