kneser-ney | Kneser-Ney implementation in Python | Build Tool library

by smilli Python Version: Current License: No License

X-Ray Key Features Code Snippets Community Discussions(2)Vulnerabilities Install Support

kandi X-RAY | kneser-ney Summary

kneser-ney is a Python library typically used in Utilities, Build Tool, Numpy applications. kneser-ney has no bugs, it has no vulnerabilities and it has low support. However kneser-ney build file is not available. You can download it from GitHub.

Kneser-Ney implementation in Python

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kneser-ney has a low active ecosystem.

It has 67 star(s) with 31 fork(s). There are 1 watchers for this library.

It had no major release in the last 6 months.

There are 6 open issues and 0 have been closed. On average issues are closed in 859 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of kneser-ney is current.

Quality

kneser-ney has 0 bugs and 3 code smells.

Security

kneser-ney has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

kneser-ney code analysis shows 0 unresolved vulnerabilities.

There are 1 security hotspots that need review.

License

kneser-ney does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

kneser-ney releases are not available. You will need to build from source code and install.

kneser-ney has no build file. You will be need to create the build yourself to build the component from source.

Installation instructions are not available. Examples and code snippets are available.

It has 134 lines of code, 15 functions and 2 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed kneser-ney and discovered the below as its top functions. This is intended to give you an instant insight into kneser-ney implemented functionality, and help decide if they suit your requirements.

Train the model
Calculate the adjacency counts
Calculates discounts for the given counts
Calculate backoff probabilities
Interpolate the given orders
Returns the discount for a given count
Calculate the probability of the given unigrams
Generate a sentence
Generate next word
Return the context of the sentence
The highest order probability of the model
Compute the log probability of a sentence
Returns the log probability of ngram

Get all kandi verified functions for this library.

kneser-ney Key Features

No Key Features are available at this moment for kneser-ney.

kneser-ney Examples and Code Snippets

No Code Snippets are available at this moment for kneser-ney.

Community Discussions

Trending Discussions on kneser-ney

NLTK language modeling confusion

Python - Trigram Probability Distribution Smoothing Technique (Kneser Ney) in NLTK Returns Zero

QUESTION

NLTK language modeling confusion

Asked 2020-Jan-05 at 13:58

I want to train a language model using NLTK in python but I got into several problems. first of all, I don't know why my words turn into just characters as I write something like this :

...

ANSWER

Answered 2019-Mar-02 at 20:35

The padded_everygram_pipeline function expects a list of list of n-grams. You should fix your first code snippet as follows. Also python generators are lazy sequences, you can't iterate them more than once.

Source https://stackoverflow.com/questions/54959340

QUESTION

Python - Trigram Probability Distribution Smoothing Technique (Kneser Ney) in NLTK Returns Zero

Asked 2019-Jul-15 at 11:07

I have the frequency distribution of my trigram followed by training the Kneser-Ney. When I check for kneser_ney.prob of a trigram that is not in the list_of_trigrams I get zero! What am I doing wrong?

...

ANSWER

Answered 2019-Jul-15 at 11:07

I think what you are observing is perfectly normal.

From the Wikipedia page (method section) for Kneser-Ney smoothing:

Please note that p_KN is a proper distribution, as the values defined in above way are non-negative and sum to one.

and the probability is 0 when the ngram did not occurred in corpus.

Quoting from the answer you cite:

This is the whole point of smoothing, to reallocate some probability mass from the ngrams appearing in the corpus to those that don't so that you don't end up with a bunch of 0 probability ngrams.

The above sentence does not mean that with Kneser-Ney smoothing you will have a non-zero probability for any ngram you pick, it means that, given a corpus, it will assign a probability to existing ngrams in such a way that you have some spare probability to use for other ngrams in later analyses. This spare probability is something you have to assign for non-occurring ngrams, not something that is inherent to the Kneser-Ney smoothing.

EDIT

Just for the sake of completeness I report the code to observe the behavior (largely taken from here, and adapted to Python 3):

Source https://stackoverflow.com/questions/57017064

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install kneser-ney

You can download it from GitHub.
You can use kneser-ney like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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