TextRank | using pre-trained Word2Vec embeddings | Natural Language Processing library

by naiveHobo Python Version: Current License: MIT

X-Ray Key Features Code Snippets Community Discussions(4)Vulnerabilities Install Support

kandi X-RAY | TextRank Summary

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

Implementation of TextRank with the option of using cosine similarity of word vectors from pre-trained Word2Vec embeddings as the similarity metric.

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Quality

Security

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Reuse

Support

TextRank has a low active ecosystem.

It has 43 star(s) with 10 fork(s). There are 2 watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 0 have been closed. On average issues are closed in 45 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of TextRank is current.

Quality

TextRank has 0 bugs and 0 code smells.

Security

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

TextRank code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

TextRank is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

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

TextRank 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.

Top functions reviewed by kandi - BETA

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

Extract keywords from text
Extract tokens from lemmas
Add a node to the graph
Format the results
Cleans the given text
Return part of speech tag

Get all kandi verified functions for this library.

TextRank Key Features

No Key Features are available at this moment for TextRank.

TextRank Examples and Code Snippets

No Code Snippets are available at this moment for TextRank.

Community Discussions

Trending Discussions on TextRank

R: Converting Tibbles to a Term Document Matrix

R: Error in textrank_sentences(data = article_sentences, terminology = article_words) : nrow(data) > 1 is not TRUE

Separate sentences ending with a scientific reference number in r

Implementation of TextRank algorithm using Spark(Calculating cosine similarity matrix using spark)

QUESTION

R: Converting Tibbles to a Term Document Matrix

Asked 2021-Apr-09 at 06:39

I am using the R programming language. I learned how to take pdf files from the internet and load them into R. For example, below I load 3 different books by Shakespeare into R:

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ANSWER

Answered 2021-Apr-09 at 06:39

As the error message suggests, VectorSource only takes 1 argument. You can rbind the datasets together and pass it to VectorSource function.

Source https://stackoverflow.com/questions/67016046

QUESTION

R: Error in textrank_sentences(data = article_sentences, terminology = article_words) : nrow(data) > 1 is not TRUE

Asked 2021-Apr-07 at 05:11

I am using the R programming language. I am trying to learn how to summarize text articles by using the following website: https://www.hvitfeldt.me/blog/tidy-text-summarization-using-textrank/

As per the instructions, I copied the code from the website (I used some random PDF I found online):

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ANSWER

Answered 2021-Apr-07 at 05:11

The link that you shared reads the data from a webpage. div[class="padded"] is specific to the webpage that they were reading. It will not work for any other webpage nor the pdf from which you are trying to read the data. You can use pdftools package to read data from pdf.

Source https://stackoverflow.com/questions/66979242

QUESTION

Separate sentences ending with a scientific reference number in r

Asked 2021-Mar-05 at 05:04

I am working on a project where one of the steps is to separate text of scientific articles into sentences. For this, I am using textrank which I understands it looks for . or ? or ! etc. to identify end of the sentence of tokenization.

The problem I am running into is sentences that end with a period followed directly by a reference number (that also might be in brackets). The examples below represent the patterns I identified and collected so far.

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ANSWER

Answered 2021-Mar-05 at 05:04

For the exact sample inputs you gave us, you may do a regex search on the following pattern:

Source https://stackoverflow.com/questions/66487031

QUESTION

Implementation of TextRank algorithm using Spark(Calculating cosine similarity matrix using spark)

Asked 2020-Jul-20 at 16:24

I am trying to implement textrank algorithm where I am calculating cosine-similarity matrix for all the sentences.I want to parallelize the task of similarity matrix creation using Spark but don't know how to implement it.Here is the code:

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ANSWER

Answered 2020-Jul-20 at 16:24

The experiments with large scale matrix calculation for cosine similarity are well written in here!

To achieve speed and not compromising much on the accuracy, you can also try hashing methods like Min-Hash and evaluate Jaccard Distance similarity. It comes with a nice implementation with Spark ML-lib, the documentation has very detailed examples for reference: http://spark.apache.org/docs/latest/ml-features.html#minhash-for-jaccard-distance

Source https://stackoverflow.com/questions/62988767

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

Vulnerabilities

No vulnerabilities reported

Install TextRank

You can download it from GitHub.
You can use TextRank 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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