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Titlebook: Chinese Language Resources; Data Collection, Lin Chu-Ren Huang,Shu-Kai Hsieh,Peng Jin Book 2023 Springer Nature Switzerland AG 2023 languag

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41#
發(fā)表于 2025-3-28 17:57:26 | 只看該作者
42#
發(fā)表于 2025-3-28 19:24:46 | 只看該作者
43#
發(fā)表于 2025-3-28 23:21:35 | 只看該作者
44#
發(fā)表于 2025-3-29 04:02:31 | 只看該作者
Reaktive Sauerstoffspezies in der Medizinor segmentation, we propose a semi-supervised approach, which performs online learning with either labeled or unlabeled data. This approach adopts the word boundary decision (WBD) model and is capable of using only the bigram information of the target article to train for better performance in almos
45#
發(fā)表于 2025-3-29 08:26:01 | 只看該作者
Reaktive Sauerstoffspezies in der Medizin and annotations. In our study, we focused on how the constituents in a fossilized composition like an idiom affect semantic and grammatical properties. As an important Chinese language resource, our knowledge base of Chinese idiomatic expressions is expected to play a major role in practices such a
46#
發(fā)表于 2025-3-29 13:20:05 | 只看該作者
47#
發(fā)表于 2025-3-29 17:42:20 | 只看該作者
48#
發(fā)表于 2025-3-29 22:38:16 | 只看該作者
Chinese Comprehensive Language Knowledge Basell introduce the rationale for and the process of constructing the Comprehensive Language Knowledge Base (CLKB) by the Institute of Computational Linguistics at Peking University (ICL/PKU), as well as illustrate its importance as the infrastructure for Chinese information processing (CIP) in various domains.
49#
發(fā)表于 2025-3-30 01:09:16 | 只看該作者
Introduction to CKIP’s Language Resources and Their Applicationsus, Sinica Chinese Treebank, and Chinese Sketch Engine), and online Chinese word segmentation and parsing systems. After a brief overview, we will show how some of these resources can be employed to generate natural language processing (NLP) applications using machine learning algorithms.
50#
發(fā)表于 2025-3-30 07:45:25 | 只看該作者
Sense Tagging Unknown Chinese Words with Word Embeddingfixes were used to filter out the semantic categories that were inconsistent with the unknown words in terms of POS and suffixes. The model then used voting to select the best sense category. Our experiments showed that the model based on a combination of external features and internal features achieved good precision in sense tagging.
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