吼叫
发表于 2025-3-23 12:41:31
Russian Q&A Method Study: From Naive Bayes to Convolutional Neural Networksnal neural network for question classification. We took advantage of an existing corpus of 2008 questions, manually annotated in accordance with a pragmatic 14-class typology. We modified the data by reducing the typology to 13 classes, expanding the dataset and improving the representativeness of s
escalate
发表于 2025-3-23 17:44:54
Extraction of Explicit Consumer Intentions from Social Network Messageswork users to purchase certain goods or use certain services. The utilized approach is machine learning with annotation. A training set for expert annotation consists of messages from the “VKontakte” social network, selected through the LeadScanner API. The invented system of semantic tags allows di
衍生
发表于 2025-3-23 21:38:29
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不整齐
发表于 2025-3-24 00:39:37
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常到
发表于 2025-3-24 05:57:17
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不知疲倦
发表于 2025-3-24 09:06:53
https://doi.org/10.1007/978-3-658-28741-2s, administrative support from the head office to subsidiaries, and levels of subsidiary integration. This is because social relationships between different actors inside the organization, the strength of ties and the size of networks, as well as other characteristics, could be the explanatory varia
BULLY
发表于 2025-3-24 13:24:07
https://doi.org/10.1007/978-3-658-16277-1nd we also build models for determining the sentiment intensity for individual modalities and a combination of them. Different classification algorithms are compared: linear, neural networks and ensembles of decision trees, where XGBoost works best for audio, Logistic Regression - for text and Light
justify
发表于 2025-3-24 16:22:00
Inequality and the Digital Economy% accuracy on the new dataset). We also tested several widely-used machine learning methods (logistic regression, Bernoulli Naïve Bayes) trained on the new question representation. The best result of 72.38% accuracy (micro) was achieved with the CNN model. We also ran experiments on pertinent featur
闯入
发表于 2025-3-24 22:25:02
https://doi.org/10.1007/978-3-319-78420-5es of its main word. The edges of the graph connect the intentional blocks that can be found in adjacent positions across all the messages of the training set. Extraction of intention objects and their properties is achieved by test set analysis in accordance to the constructed graph. Test set inclu
etiquette
发表于 2025-3-25 02:32:57
Lorenzo Pupillo,Eli Noam,Leonard Waverman embeddings from the E-step. Second, we show that Biterm Topic Model (Yan et al. [.]) and Word Network Topic Model (Zuo et al. [.]) are equivalent with the only difference of tying word and context embeddings. We further extend these models by adjusting representation of each sliding window with a f