PDF] Near-Synonym Choice using a 5-gram Language Model
Por um escritor misterioso
Last updated 16 abril 2025
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An unsupervised statistical method for automatic choice of near-synonyms is presented and compared to the stateof-the-art and it is shown that this method outperforms two previous methods on the same task. In this work, an unsupervised statistical method for automatic choice of near-synonyms is presented and compared to the stateof-the-art. We use a 5-gram language model built from the Google Web 1T data set. The proposed method works automatically, does not require any human-annotated knowledge resources (e.g., ontologies) and can be applied to different languages. Our evaluation experiments show that this method outperforms two previous methods on the same task. We also show that our proposed unsupervised method is comparable to a supervised method on the same task. This work is applicable to an intelligent thesaurus, machine translation, and natural language generation.
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Transtheoretical model - Wikipedia
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The 5 Stages in the Design Thinking Process
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PDF] Near-Synonym Choice using a 5-gram Language Model
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Language Model Concept behind Word Suggestion Feature, by Vitou Phy
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Applied Sciences, Free Full-Text
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Fill and sign PDF forms using Adobe Acrobat Fill & Sign tool
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Color Theory 101: A Complete Guide to Color Wheels & Color Schemes
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N-Gram Language Model
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Sample Synonym, PDF
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What Is SEO - Search Engine Optimization?
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Kohlberg's Stages of Moral Development
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N-gram language models. Part 1: The unigram model, by Khanh Nguyen, MTI Technology
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What Is ChatGPT Doing … and Why Does It Work?—Stephen Wolfram Writings
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