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.

Large language models encode clinical knowledge

What Is SEO - Search Engine Optimization?

Decision Tree Analysis Examples and How to Use Them - Venngage

n-gram language model - an overview

Retrosynthesis prediction with an interpretable deep-learning framework based on molecular assembly tasks

Heckscher-Ohlin Model Definition: Evidence and Real-World Example

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

Near-synonym choice using a 5-gram language model

PDF] Near-synonymy and the structure of lexical knowledge

What Is ChatGPT Doing … and Why Does It Work?—Stephen Wolfram Writings

Sample Synonym, PDF

Tirzepatide versus Semaglutide Once Weekly in Patients with Type 2 Diabetes

N-Gram Model

IS-LM Model: What It Is, IS and LM Curves, Characteristics, Limitations

Leveraging molecular structure and bioactivity with chemical language models for de novo drug design