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.

N-Gram Model

Class 10 Maths Chapter 2 Polynomials MCQs (With Answers)

Large language models encode clinical knowledge

What is a Language Model: Introduction, Use Cases

n-gram - Wikipedia

Decision Tree Analysis Examples and How to Use Them - Venngage

N-Grams and Language Modeling - ppt download

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

Near-synonym choice using a 5-gram language model

Kohlberg's Stages of Moral Development

N-Gram Language Model

Language Model Concept behind Word Suggestion Feature, by Vitou Phy

14 Context Clues Anchor Charts For the Classroom - We Are Teachers