Occlusion
发表于 2025-3-25 05:35:17
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Culmination
发表于 2025-3-25 10:32:13
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Hdl348
发表于 2025-3-25 11:40:40
https://doi.org/10.1007/978-981-10-7697-8omputer-readable documents). To highlight in the text of people, organizations, geographical locations, etc., many approaches are used. Although, well-known bidirectional LSTM neural networks, show good results, there are points for improvement. Usually, the word embedding vector are used as the inp
原谅
发表于 2025-3-25 19:18:46
Alan Rosling,Kathryn Littlemore also estimates the cost of all sanctions - both for those who receive and those who impose them. As input variables for DEA model we use the impact of sender commitment, anticipated target and sender economic costs, and actual target and sender economic costs. As the output variable, we use the out
陪审团每个人
发表于 2025-3-25 22:47:09
Electronic Theses and Dissertationshe base model. The obtained results demonstrate quality on par with state-of-the-art systems, which serves to re-establish the importance of semantic features in coreference resolution, as well as the applicability of neural networks for the task.
Injunction
发表于 2025-3-26 02:30:08
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有帮助
发表于 2025-3-26 05:36:33
https://doi.org/10.1007/978-981-10-7697-8used in our work in two modes: feature extraction and fine-tuning for the NER task. Evaluation of the results was carried out on the FactRuEval dataset and the BiLSTM network (FastText + CNN + extra) was taken as the baseline. Our model, built on fine-tuned deep contextual BERT model, shows good res
BUDGE
发表于 2025-3-26 12:16:08
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肉身
发表于 2025-3-26 13:31:51
Using Semantic Information for Coreference Resolution with Neural Networks in Russianhe base model. The obtained results demonstrate quality on par with state-of-the-art systems, which serves to re-establish the importance of semantic features in coreference resolution, as well as the applicability of neural networks for the task.
慎重
发表于 2025-3-26 17:01:14
Recognition of Parts of Speech Using the Vector of Bigram Frequenciesequencies of syntactic bigrams including the test word and one of the 10.000 most frequent words was at the inputs of the network. The neural network was trained by the criterion of minimum cross–entropy. When recognizing parts of speech on the test sample, the average recognition accuracy was 99.1%