textrank | Java implementation of the TextRank algorithm

by samxhuan Java Version: Current License: BSD-3-Clause

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

kandi X-RAY | textrank Summary

textrank is a Java library. 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.

Open source Java implementation of the TextRank algorithm by Mihalcea, et al. Author: Paco NATHAN paco@sharethis.com. GitHub code repo: NB: there is a known issue with use of JWNL (Java libraries for WordNet) such that if the graph size exceeds a particular threshold, then low-level Java I/O reads to the WordNet database on disk will cause Java thread to block -- even though JVM tools show no blocked threads. A potential remedy is to dump WordNet, or at least the parts of it used here, into some DBD structure with an in-memory cache. simple test: ant run. test with a specific data file FOO.txt. build the JAR for export to another project: ant jar.

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Support

textrank has a low active ecosystem.

It has 30 star(s) with 58 fork(s). There are 4 watchers for this library.

It had no major release in the last 6 months.

textrank has no issues reported. There are 1 open pull requests and 0 closed 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 BSD-3-Clause 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.

It has 1576 lines of code, 92 functions and 18 files.

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

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.

Run the TextRank algorithm
Generate tokens for the given language
Build a Ngram from a sentence
Iterate over the graph
Build the dictionary
Builds a properties stream from the configuration file
Serialize the metric graph to a string
Render the metric
Main entry point
Prepare to call
Serialize the graph to a file
Emits the graph representation of the graph
Tag a sentence
Tokenize a sentence
Compare n
Create a description text for this synset
Builds the Language object for OpenNLP
Load the resources for OpenNLP
Builds a Language model for the given language code
Load the library for OpenNLP resources

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 Java library. Please include the the jar files in your classpath. You can also use any IDE and you can run and debug the textrank component as you would do with any other Java program. Best practice is to use a build tool that supports dependency management such as Maven or Gradle. For Maven installation, please refer maven.apache.org. For Gradle installation, please refer gradle.org .

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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