PDF] Near-Synonym Choice using a 5-gram Language Model

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.

One model for the learning of language

N-gram language models. Part 1: The unigram model, by Khanh Nguyen, MTI Technology

PDF] Near-Synonym Choice using a 5-gram Language Model

Fill and sign PDF forms using Adobe Acrobat Fill & Sign tool

Modulation of dopamine tone induces frequency shifts in cortico-basal ganglia beta oscillations

Near-synonym choice using a 5-gram language model

Sample Synonym, PDF

How to Make an Infographic in Under 1 Hour (2023 Guide) - Venngage

Solved Final Project N-Gram Language Models In the textbook

n-gram language model - an overview

N-Gram Language Model

Apples: Benefits, nutrition, and tips

N-gram language models. Part 2: Higher n-gram models, by Khanh Nguyen, MTI Technology

The COM-B Model for Behavior Change - The Decision Lab