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Titlebook: Linguistic Resources for Natural Language Processing; On the Necessity of Max Silberztein Book 2024 The Editor(s) (if applicable) and The

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發(fā)表于 2025-3-21 18:56:15 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱(chēng)Linguistic Resources for Natural Language Processing
副標(biāo)題On the Necessity of
編輯Max Silberztein
視頻videohttp://file.papertrans.cn/587/586699/586699.mp4
概述Addresses the topic of multiword units in NLP software and the issue low-resource languages.Discusses training corpus-based approaches and explains the intrinsic value of linguistic formalization.Show
圖書(shū)封面Titlebook: Linguistic Resources for Natural Language Processing; On the Necessity of Max Silberztein Book 2024 The Editor(s) (if applicable) and The
描述.Empirical — data-driven, neural network-based, probabilistic, and statistical — methods seem to be the modern trend. Recently, OpenAI’s ChatGPT, Google’s Bard and Microsoft’s Sydney chatbots have been garnering a lot of attention for their detailed answers across many knowledge domains. In consequence, most AI researchers are no longer interested in trying to understand what common intelligence is or how intelligent agents construct scenarios to solve various problems. Instead, they now develop systems that extract solutions from massive databases used as cheat sheets. In the same manner, Natural Language Processing (NLP) software that uses training corpora associated with empirical methods are trendy, as most researchers in NLP today use large training corpora, always to the detriment of the development of formalized dictionaries and grammars..Not questioning the intrinsic value of many software applications based on empirical methods, this volume aims at rehabilitating the linguistic approach to NLP. In an introduction, the editor uncovers several limitations and flaws of using training corpora to develop NLP applications, even the simplest ones, such as automatic taggers..The f
出版日期Book 2024
關(guān)鍵詞Named Entity Recognition; Natural Language Processing; Text Generation; Training Corpora; Statistical Me
版次1
doihttps://doi.org/10.1007/978-3-031-43811-0
isbn_softcover978-3-031-43813-4
isbn_ebook978-3-031-43811-0
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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發(fā)表于 2025-3-21 23:13:09 | 只看該作者
Formalization of the Quechua Morphologyventory of all Quechua suffixes, I classified them into specific sets corresponding to their POS category. Next, I formalized their grammatical behavior separately, using elementary matrices. The resulting tables describe valid combinations of two, three, and four suffixes. Finally, I formalized the inflection and derivation of each POS category.
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發(fā)表于 2025-3-22 00:38:59 | 只看該作者
地板
發(fā)表于 2025-3-22 07:38:54 | 只看該作者
The Limitations of Corpus-Based Methods in NLPs. Next, I examine the principles which are at the basis of corpus-based methods and uncover their linguistic naiveté. I finally dispute the scientific validity of empirical approaches. I propose solutions to various problems that are based on the use of carefully handcrafted linguistic methods and resources.
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發(fā)表于 2025-3-22 10:51:02 | 只看該作者
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發(fā)表于 2025-3-22 15:10:19 | 只看該作者
A Polylectal Linguistic Resource for Rromanirammar that describes agglutination. It can be used both for Rromani language studies and for developing Natural Language Processing (NLP) applications. We show that the same architecture can describe other low-resource languages.
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發(fā)表于 2025-3-22 17:54:21 | 只看該作者
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發(fā)表于 2025-3-22 23:59:19 | 只看該作者
Linguistic Resources and Methods for Belarusian Natural Language Processing for computational processing of textual information and speech. The proposed resources are also used to collect targeted thematic content to develop and refine natural language processing systems for Belarusian. As a consequence, we show that linguistic resources have not lost their relevance to NLP.
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發(fā)表于 2025-3-23 02:12:45 | 只看該作者
A New Set of Linguistic Resources for Ukrainiancial sciences to analyze their own corpora of Ukrainian texts. We will first review the various existing NLP software applications that can process Ukrainian texts, their functionalities, and their performance. We then describe the linguistic resources we have developed, and finally compare the results produced by both approaches.
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發(fā)表于 2025-3-23 09:05:27 | 只看該作者
The Challenging Task of Translating the Language of Tangoof translating a Tango song lyrics to English. We look at the translations produced by Google Translator and DeepL and we compare them with translations produced by accessing handcrafted linguistic resources specifically developed for Rioplatense Spanish.
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