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Titlebook: Natural Language Processing and Chinese Computing; Second CCF Conferenc Guodong Zhou,Juanzi Li,Yansong Feng Conference proceedings 2013 Spr

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41#
發(fā)表于 2025-3-28 16:27:18 | 只看該作者
Structure-Based Web Access Method for Ancient Chinese Characters knowledge about ancient Chinese characters. We also design a system suitable for describing the relationships between ancient Chinese characters and contemporary ones. As the implementation result, a website is established for public access to ancient Chinese characters.
42#
發(fā)表于 2025-3-28 22:40:10 | 只看該作者
43#
發(fā)表于 2025-3-28 23:51:25 | 只看該作者
Conference proceedings 2013omputing; machine learning for NLP; machine translation and multi-lingual information access; NLP for social media and web mining, knowledge acquisition; NLP for search technology and ads; NLP fundamentals; NLP applications; NLP for social media.
44#
發(fā)表于 2025-3-29 04:13:32 | 只看該作者
45#
發(fā)表于 2025-3-29 11:01:34 | 只看該作者
Text Window Denoising Autoencoder: Building Deep Architecture for Chinese Word Segmentationo various Chinese natural language processing tasks, such as Chinese word segmentation. On the PKU dataset of Chinese word segmentation bakeoff 2005, applying this method decreases the F1 error rate by 11.9% for deep neural network based models. We are the first to apply deep learning methods to Chinese word segmentation to our best knowledge.
46#
發(fā)表于 2025-3-29 12:33:49 | 只看該作者
47#
發(fā)表于 2025-3-29 18:36:50 | 只看該作者
Chinese Negation and Speculation Detection with Conditional Random Fieldsperimental results show that the single-word feature and the part of speech feature are effective, and the combined features improve the performance furthest. Our Chinese negation and speculation detection system in sentence level achieves 94.70% and 87.10% of accuracy, respectively.
48#
發(fā)表于 2025-3-29 22:08:54 | 只看該作者
49#
發(fā)表于 2025-3-30 02:05:39 | 只看該作者
50#
發(fā)表于 2025-3-30 06:02:53 | 只看該作者
Incorporating Entities in News Topic Modelingpics by taking entity topic as a mixture of word topics. Experiments on real news data sets show our model of a lower perplexity and better in clustering of entities than state-of-the-art entity topic model(CorrLDA2). We also present analysis for results of ECTM and further compare it with CorrLDA2.
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