PDF] Near-Synonym Choice using a 5-gram Language Model
Por um escritor misterioso
Last updated 31 março 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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N-gram Language Modeling in Natural Language Processing - KDnuggets
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One model for the learning of language
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N-Gram Model
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n-gram - Wikipedia
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Language Model Concept behind Word Suggestion Feature, by Vitou Phy
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Transtheoretical model - Wikipedia
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Algorithm - Wikipedia
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What Is SEO - Search Engine Optimization?
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Decision Tree Analysis Examples and How to Use Them - Venngage
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Apples: Benefits, nutrition, and tips
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PDF] Near-Synonym Choice using a 5-gram Language Model
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