keyword_extraction | Use Python to extract Chinese text keywords | Natural Language Processing library

by AimeeLee77 Python Version: Current License: No License

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | keyword_extraction Summary

keyword_extraction is a Python library typically used in Artificial Intelligence, Natural Language Processing applications. keyword_extraction has no bugs, it has no vulnerabilities and it has medium support. However keyword_extraction build file is not available. You can download it from GitHub.

Use Python to extract Chinese text keywords, using three methods of TF-IDF, TextRank, and Word2Vec word clustering.

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keyword_extraction has a medium active ecosystem.

It has 999 star(s) with 365 fork(s). There are 24 watchers for this library.

It had no major release in the last 6 months.

There are 9 open issues and 3 have been closed. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of keyword_extraction is current.

Quality

keyword_extraction has 0 bugs and 0 code smells.

Security

keyword_extraction has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

keyword_extraction code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

keyword_extraction 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.

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keyword_extraction releases are not available. You will need to build from source code and install.

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

keyword_extraction saves you 81 person hours of effort in developing the same functionality from scratch.

It has 209 lines of code, 11 functions and 4 files.

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

Top functions reviewed by kandi - BETA

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

Get k - means k - means clustering .
get keywords from tfidf
Main function for the main function .
calculates the textRank based on the text rank
Builds all words in the vocabulary
Given a list of word vectors and a model returns a pandas dataframe .
Return a list of the word prepos in text .

Get all kandi verified functions for this library.

keyword_extraction Key Features

No Key Features are available at this moment for keyword_extraction.

keyword_extraction Examples and Code Snippets

No Code Snippets are available at this moment for keyword_extraction.

Community Discussions

Trending Discussions on keyword_extraction

Is there a way to speed up the function call with concurrency?

QUESTION

Is there a way to speed up the function call with concurrency?

Asked 2020-Dec-09 at 01:37

def process_file(csv_file_name):
    with open(csv_file_name, 'r') as f:
        csvreader = csv.DictReader(f)

        for i, row in enumerate(csvreader):
            results = keyword_extraction(row['text'])
            print(i, results['status'])

def keyword_extraction(text):
    results = []
    ...

    return results

...

ANSWER

Answered 2020-Dec-09 at 01:35

Here is an example of multiprocessing.

Source https://stackoverflow.com/questions/65209047

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

Vulnerabilities

No vulnerabilities reported

Install keyword_extraction

You can download it from GitHub.
You can use keyword_extraction 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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