Natural Language-Based Naive Bayes Classifier Model for Sentence Classification

被引：1

作者：

Yadav, Amita ^{[1
,2
]}

Rathee, Sonia ^{[1
,2
]}

Shalu ^{[1
,2
]}

Zafar, Sherin ^{[3
]}

机构：

[1] Maharaja Surajmal Inst Technol, New Delhi, India

[2] Maharaja Surajmal Inst Technol, New Delhi, India

[3] Jamia Hamdard, New Delhi, India

来源：

INTERNATIONAL CONFERENCE ON INNOVATIVE COMPUTING AND COMMUNICATIONS, ICICC 2022, VOL 1 | 2023年 / 473卷

关键词：

Text classification; Naive Bayes; Machine learning algorithm; Sentence detection;

D O I：

10.1007/978-981-19-2821-5_41

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The classification of text is one of the basic tasks of natural language processing with wide-ranging applications. This is essentially a process of assigning markers or categories to the text based on its content. The paper aims to use an improved Naive Bayes classifier to identify the fact-worthy sentence. In this paper, authors have implemented an improved Naive Bayes classifier through which we classify the sentences. This proposed method has been tested with the claim buster dataset contains 23,533 sentences where each sentence belongs to either of these three classes, i.e., non-factual statement, unimportant factual statement, and check-worthy factual statement.

引用

页码：499 / 508

页数：10

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