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International Journal of Asian Language Processing最新文献

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Embarking on a Preliminary Exploration: Cross-Lingual Embedding in English-Manipuri 开始初步探索:英语-曼尼普尔语中的跨语言嵌入
Pub Date : 2024-07-12 DOI: 10.1142/s2717554524500073
Deepen Naorem, Sanasam Ranbir Singh, Priyankoo Sarmah
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引用次数: 0
A Study on the Utilizing of Durational and Spectral Cues in the Perception of English /i-i:/ Contrast by Chinese Learners 中国学习者在感知英语/i-i:/对比时利用时长和频谱线索的研究
Pub Date : 2024-07-12 DOI: 10.1142/s2717554524500061
Yuhang Pei, Jian Gong
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引用次数: 0
Early Segmental Production in Thai Preschool Children Learning Mandarin 泰国学龄前儿童学习普通话时的早期分段发音
Pub Date : 2024-07-05 DOI: 10.1142/s271755452450005x
I-Ping Wan, Marc Allassonnière-Tang, Pu Yu
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引用次数: 0
The Construction of a Large-Scale Hong Kong Chinese Lexicon with Multilingual Translations for Chinese-as-an-Additional-Language (CAL) Students 为汉语作为辅助语言(CAL)学生构建包含多语种翻译的大型香港汉语词典
Pub Date : 2023-11-17 DOI: 10.1142/s2717554523500182
Chaak-ming Lau, Wai Sum Sandy Leung
. This paper describes the structure and compilation process of a Hong Kong Chinese Lexicon being developed for a forthcoming Cantonese keyboard. The lexicon contains more than 120,000 headwords including common words and high-frequency named entities, and incorporates information such as Jyutping romanization, indicative word frequency, simple translations in five languages (English, Hindi, Indonesian, Nepali, Urdu), disambiguation, POS information, speech register and cross-register synonyms. The lexicon improves the Chinese learning experience of CAL students in two ways, (a) to introduce and promote the effective usage of input methods and dictionaries for learners that facilitate them to locate the correct choice of word or phrase within a particular context, and (b) to provide additional register-specific data that enables advanced learners to further expand their vocabulary. This lexicon can be used in conjunction with wordnet or LIWC (Linguistic Inquiry and Word Count) for a range of natural language processing tasks.
.本文介绍了为即将推出的粤语键盘开发的香港中文词典的结构和编纂过程。词库包含超过 120,000 个词头,包括常用词和高频命名实体,并整合了多种信息,如汉语拼音罗马化、指示词频、五种语言(英语、印地语、印尼语、尼泊尔语、乌尔都语)的简单翻译、歧义、POS 信息、语域和跨语域同义词。词库从两个方面改善了 CAL 学生的汉语学习体验:(a) 向学习者介绍并促进输入法和词典的有效使用,帮助他们在特定语境中找到正确的词或短语选择;(b) 提供额外的语域特定数据,帮助高级学习者进一步扩大词汇量。该词典可与单词网或 LIWC(语言调查和单词计数)结合使用,用于一系列自然语言处理任务。
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引用次数: 0
Effects of Synthesized Inflectional Tones on the Perception of Mandarin Third Tone by Native Chinese Speakers and Korean L2 Learners 合成语气助词对汉语母语者和韩国语第二语言学习者感知普通话第三声的影响
Pub Date : 2023-11-17 DOI: 10.1142/s2717554523500133
Jung-yueh Tu, Chen Li
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引用次数: 0
Contextual Adversarial Representation Learning for Multiword Expressions 多词表达的上下文对抗表征学习
Pub Date : 2023-11-17 DOI: 10.1142/s2717554523500145
Bo An
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引用次数: 0
AI-Generated Text Detection: Challenges and Future Directions 人工智能生成的文本检测:挑战与未来方向
Pub Date : 2023-11-17 DOI: 10.1142/s2717554523300025
Bo An, Congjun Long
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引用次数: 0
Refining Sentiment Predictions: Obtaining an Unbiased Business Sentiment Index from Japanese Newspapers 完善情绪预测:从日本报纸中获取无偏见的商业情绪指数
Pub Date : 2023-11-17 DOI: 10.1142/s2717554523500157
Kazuhiro Seki
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引用次数: 0
Maximal-Semantics-Augmented BertGCN for Text Classification 用于文本分类的最大语义增强 BertGCN
Pub Date : 2023-11-17 DOI: 10.1142/s2717554523500169
Xiaoqi Yang, Wuying Liu
. Text classification is an important research work in the fields of natural language processing (NLP), and many methods of machine learning and deep learning are widely used in this work. In this paper, we propose a method which named Maximal-Semantics-Augmented BertGCN based on BertGCN that further improves the results of text categorization tasks. In this work, the extended semantic information of text is utilized more effectively by means of text semantic enhancement and graph nodes enhancement while preserving the original text features. Four datasets commonly used in the fields of text classification named R8, R52, Ohsumed and MR were used to verify the validity of the method we proposed. Experimental results show that compared with BertGCN and other baselines, the proposed method which named MSABertGCN has varying degrees of improvement in the accuracy of R8, R52, Ohsumed and MR datasets.
.文本分类是自然语言处理(NLP)领域的一项重要研究工作,许多机器学习和深度学习方法被广泛应用于这项工作中。本文在 BertGCN 的基础上提出了一种名为 Maximal-Semantics-Augmented BertGCN 的方法,进一步提高了文本分类任务的结果。在这项工作中,通过文本语义增强和图节点增强,在保留原始文本特征的同时,更有效地利用了文本的扩展语义信息。为了验证我们所提方法的有效性,我们使用了 R8、R52、Ohsumed 和 MR 四个文本分类领域常用的数据集。实验结果表明,与 BertGCN 和其他基线方法相比,所提出的 MSABertGCN 方法在 R8、R52、Ohsumed 和 MR 数据集上的准确率都有不同程度的提高。
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引用次数: 0
Scientific-Linguistic Framework: Identification of Tonal Relations 科学-语言框架:声调关系的识别
Pub Date : 2023-09-01 DOI: 10.1142/s2717554523500121
Yishan Huang
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引用次数: 0
期刊
International Journal of Asian Language Processing
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