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Titlebook: Artificial Neural Networks and Machine Learning – ICANN 2019: Text and Time Series; 28th International C Igor V. Tetko,Věra K?rková,Fabian

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31#
發(fā)表于 2025-3-26 22:40:08 | 只看該作者
Materials Integration Strategies,s, ranging from Computer Vision to Natural Language Processing. In this paper we focus on Facebook posts paired with “reactions” of multiple users, and we investigate their relationships with classes of emotions that are typically considered in the task of emotion detection. We are inspired by the i
32#
發(fā)表于 2025-3-27 03:22:11 | 只看該作者
https://doi.org/10.1007/978-3-030-30490-4artificial intelligence; classification; clustering; computational linguistics; computer networks; Human-
33#
發(fā)表于 2025-3-27 09:21:39 | 只看該作者
34#
發(fā)表于 2025-3-27 09:35:33 | 只看該作者
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/b/image/162646.jpg
35#
發(fā)表于 2025-3-27 16:07:45 | 只看該作者
Artificial Neural Networks and Machine Learning – ICANN 2019: Text and Time Series978-3-030-30490-4Series ISSN 0302-9743 Series E-ISSN 1611-3349
36#
發(fā)表于 2025-3-27 18:55:01 | 只看該作者
An Ensemble Model for Winning a Chinese Machine Reading Comprehension Competition a Chinese machine reading comprehension competition, namely the LES Cup Challenge, in October 2018. The competition introduces a big dataset of long articles and improperly labelled data, therefore challenges the state-of-the-art methods in this area. We proposed an ensemble model of four novel rec
37#
發(fā)表于 2025-3-28 00:01:46 | 只看該作者
38#
發(fā)表于 2025-3-28 04:37:48 | 只看該作者
Learning to Explain Chinese Slang Words has affected the accuracy of reading comprehension and word segmentation tasks. In this paper, we propose explaining Chinese slang word automatically for the first time. Unlike matching words in dictionary, we use a novel neural network called DCEAnn (a Dual Character-level Encoder using Attention-
39#
發(fā)表于 2025-3-28 07:54:57 | 只看該作者
40#
發(fā)表于 2025-3-28 12:48:37 | 只看該作者
An Improved Method of Applying a Machine Translation Model to a Chinese Word Segmentation TaskHowever, directly applying the MT model to CWS task would introduce translation errors and result in poor word segmentation. In this paper, we propose a novel method named Translation Correcting to solve this problem. Based on the differences between CWS and MT, Translation Correcting eliminates tra
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