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Titlebook: Computational Linguistics and Intelligent Text Processing; 16th International C Alexander Gelbukh Conference proceedings 2015 Springer Inte

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41#
發(fā)表于 2025-3-28 14:40:09 | 只看該作者
42#
發(fā)表于 2025-3-28 21:59:32 | 只看該作者
43#
發(fā)表于 2025-3-29 01:36:52 | 只看該作者
44#
發(fā)表于 2025-3-29 04:53:26 | 只看該作者
Framing the Framework: The Origins of , machines since it remains “trapped” in linguistic scholarly documents and in human readable form. In this paper, we introduce several tools that make IGT more accessible and consumable by NLP researchers.
45#
發(fā)表于 2025-3-29 09:46:55 | 只看該作者
46#
發(fā)表于 2025-3-29 12:07:46 | 只看該作者
Jan-Erik Schmidt,Caterina Gawrilow a rule-based morphology can be extended. This is achieved by predicting the correct paradigm for words not present in the lexicon. The supervised machine learning algorithm described in this paper is based on longest matching suffixes and lexical frequency data, and is demonstrated and evaluated for Russian.
47#
發(fā)表于 2025-3-29 16:34:11 | 只看該作者
Enriching, Editing, and Representing Interlinear Glossed Text machines since it remains “trapped” in linguistic scholarly documents and in human readable form. In this paper, we introduce several tools that make IGT more accessible and consumable by NLP researchers.
48#
發(fā)表于 2025-3-29 20:53:52 | 只看該作者
Comparing Neural Lexical Models of a Classic National Corpus and a Web Corpus: The Case for Russian the language of web pages from the Russian language in the National corpus. About 15% of words were found to acquire completely new neighbors in the web corpus..In this paper, the methodology of research is described and implications for Russian National Corpus are proposed. All experimental data are available online.
49#
發(fā)表于 2025-3-30 01:41:30 | 只看該作者
50#
發(fā)表于 2025-3-30 05:45:00 | 只看該作者
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