How to Install and Uninstall perl-Lingua-EN-Tagger Package on openSuSE Tumbleweed

Last updated: July 01,2024

1. Install "perl-Lingua-EN-Tagger" package

Here is a brief guide to show you how to install perl-Lingua-EN-Tagger on openSuSE Tumbleweed


    
        
            $
        

        
            
                sudo zypper refresh
            
                    
    
    Copied


    
        
            $
        

        
            
                sudo zypper install
            
                        
                perl-Lingua-EN-Tagger
            
                    
    
    Copied

2. Uninstall "perl-Lingua-EN-Tagger" package

In this section, we are going to explain the necessary steps to uninstall perl-Lingua-EN-Tagger on openSuSE Tumbleweed:


    
        
            $
        

        
            
                sudo zypper remove
            
                        
                perl-Lingua-EN-Tagger
            
                    
    
    Copied

3. Information about the perl-Lingua-EN-Tagger package on openSuSE Tumbleweed

Information for package perl-Lingua-EN-Tagger:
----------------------------------------------
Repository : openSUSE-Tumbleweed-Oss
Name : perl-Lingua-EN-Tagger
Version : 0.31-1.20
Arch : x86_64
Vendor : openSUSE
Installed Size : 2.3 MiB
Installed : No
Status : not installed
Source package : perl-Lingua-EN-Tagger-0.31-1.20.src
Upstream URL : https://metacpan.org/release/Lingua-EN-Tagger
Summary : Part-of-speech tagger for English natural language processing
Description :
The module is a probability based, corpus-trained tagger that assigns POS
tags to English text based on a lookup dictionary and a set of probability
values. The tagger assigns appropriate tags based on conditional
probabilities - it examines the preceding tag to determine the appropriate
tag for the current word. Unknown words are classified according to word
morphology or can be set to be treated as nouns or other parts of speech.
The tagger also extracts as many nouns and noun phrases as it can, using a
set of regular expressions.