1 / 1

Enhancing Text Classification with Context-Based Term Frequency Assessment

This study presents a novel technique for improving text classifiers through Context-Based Term Frequency Assessment (CTFA). By utilizing Term Context Recognition (TCR), we refine traditional term frequency assessments for various classifiers. The proposed CTFA technique is versatile and requires no modifications to existing classifiers, addressing issues of data sparseness and overfitting. Additionally, it operates with minimal memory requirements, significantly reduces computational costs, and does not necessitate domain-specific knowledge, making it accessible for various text classification tasks.

kolya
Télécharger la présentation

Enhancing Text Classification with Context-Based Term Frequency Assessment

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Title • Context-based Term Frequency Assessment for Text Classification (by Rey-Long Liu) • Goal • Improving various kinds of text classifiers by term context recognition (TCR) • Method • Employing TCR to refine term frequency (TF) assessment • Applying the TF assessment to various classifiers • Result • A technique CTFA is developed • Applicable to various kinds of text classifiers (no modification is required) • No problems of data sparseness and over-fitting • No need for huge memory, expensive computation, and domain-specific knowledge

More Related