NlpToolkit-NGram
1.0.7
dotnet add package NlpToolkit-NGram --version 1.0.7
NuGet\Install-Package NlpToolkit-NGram -Version 1.0.7
<PackageReference Include="NlpToolkit-NGram" Version="1.0.7" />
paket add NlpToolkit-NGram --version 1.0.7
#r "nuget: NlpToolkit-NGram, 1.0.7"
// Install NlpToolkit-NGram as a Cake Addin
#addin nuget:?package=NlpToolkit-NGram&version=1.0.7
// Install NlpToolkit-NGram as a Cake Tool
#tool nuget:?package=NlpToolkit-NGram&version=1.0.7
N-Gram
An N-gram is a sequence of N words: a 2-gram (or bigram) is a two-word sequence of words like “lütfen ödevinizi”, “ödevinizi çabuk”, or ”çabuk veriniz”, and a 3-gram (or trigram) is a three-word sequence of words like “lütfen ödevinizi çabuk”, or “ödevinizi çabuk veriniz”.
Smoothing
To keep a language model from assigning zero probability to unseen events, we’ll have to shave off a bit of probability mass from some more frequent events and give it to the events we’ve never seen. This modification is called smoothing or discounting.
Laplace Smoothing
The simplest way to do smoothing is to add one to all the bigram counts, before we normalize them into probabilities. All the counts that used to be zero will now have a count of 1, the counts of 1 will be 2, and so on. This algorithm is called Laplace smoothing.
Add-k Smoothing
One alternative to add-one smoothing is to move a bit less of the probability mass from the seen to the unseen events. Instead of adding 1 to each count, we add a fractional count k. This algorithm is therefore called add-k smoothing.
For Developers
You can also see Java, Python, Cython, Swift, Js, or C++ repository.
Requirements
- C# Editor
- Git
Git
Install the latest version of Git.
Download Code
In order to work on code, create a fork from GitHub page. Use Git for cloning the code to your local or below line for Ubuntu:
git clone <your-fork-git-link>
A directory called NGram-CS will be created. Or you can use below link for exploring the code:
git clone https://github.com/starlangsoftware/NGram-CS.git
Open project with Rider IDE
To import projects from Git with version control:
Open Rider IDE, select Get From Version Control.
In the Import window, click URL tab and paste github URL.
Click open as Project.
Result: The imported project is listed in the Project Explorer view and files are loaded.
Compile
From IDE
After being done with the downloading and opening project, select Build Solution option from Build menu. After compilation process, user can run NGram-CS.
Detailed Description
Training NGram
Boş bir NGram modeli oluşturmak için To create an empty NGram model:
NGram(int N)
For example,
a = NGram(2);
this creates an empty NGram model.
To add an sentence to NGram:
void AddNGramSentence(Symbol[] symbols)
For example,
string[] text1 = {"jack", "read", "books", "john", "mary", "went"};
string[] text2 = {"jack", "read", "books", "mary", "went"};
nGram = new NGram<String>(2);
nGram.AddNGramSentence(text1);
nGram.AddNGramSentence(text2);
with the lines above, an empty NGram model is created and the sentences text1 and text2 are added to the bigram model.
NoSmoothing class is the simplest technique for smoothing. It doesn't require training. Only probabilities are calculated using counters. For example, to calculate the probabilities of a given NGram model using NoSmoothing:
a.CalculateNGramProbabilities(new NoSmoothing());
LaplaceSmoothing class is a simple smoothing technique for smoothing. It doesn't require training. Probabilities are calculated adding 1 to each counter. For example, to calculate the probabilities of a given NGram model using LaplaceSmoothing:
a.CalculateNGramProbabilities(new LaplaceSmoothing());
GoodTuringSmoothing class is a complex smoothing technique that doesn't require training. To calculate the probabilities of a given NGram model using GoodTuringSmoothing:
a.CalculateNGramProbabilities(new GoodTuringSmoothing());
AdditiveSmoothing class is a smoothing technique that requires training.
a.CalculateNGramProbabilities(new AdditiveSmoothing());
Using NGram
To find the probability of an NGram:
double getProbability(params Symbol[] symbols)
For example, to find the bigram probability:
a.GetProbability("jack", "reads")
To find the trigram probability:
a.GetProbability("jack", "reads", "books")
Saving NGram
To save the NGram model:
void SaveAsText(string fileName)
For example, to save model "a" to the file "model.txt":
a.SaveAsText("model.txt");
Loading NGram
To load an existing NGram model:
NGram(string fileName)
For example,
a = NGram("model.txt");
this loads an NGram model in the file "model.txt".
Product | Versions Compatible and additional computed target framework versions. |
---|---|
.NET | net5.0 was computed. net5.0-windows was computed. net6.0 was computed. net6.0-android was computed. net6.0-ios was computed. net6.0-maccatalyst was computed. net6.0-macos was computed. net6.0-tvos was computed. net6.0-windows was computed. net7.0 was computed. net7.0-android was computed. net7.0-ios was computed. net7.0-maccatalyst was computed. net7.0-macos was computed. net7.0-tvos was computed. net7.0-windows was computed. net8.0 was computed. net8.0-android was computed. net8.0-browser was computed. net8.0-ios was computed. net8.0-maccatalyst was computed. net8.0-macos was computed. net8.0-tvos was computed. net8.0-windows was computed. |
.NET Core | netcoreapp2.2 is compatible. netcoreapp3.0 was computed. netcoreapp3.1 was computed. |
-
.NETCoreApp 2.2
- NlpToolkit-DataStructure (>= 1.0.2)
- NlpToolkit-Math (>= 1.0.5)
- NlpToolkit-Sampling (>= 1.0.3)
NuGet packages (3)
Showing the top 3 NuGet packages that depend on NlpToolkit-NGram:
Package | Downloads |
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NlpToolkit-MorphologicalDisambiguation
Package Description |
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NlpToolkit-Deasciifier
Package Description |
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NlpToolkit-SpellChecker
Package Description |
GitHub repositories
This package is not used by any popular GitHub repositories.