ai-research-keyphrase-extraction | Unsupervised Keyphrase Extraction using Sentence | Natural Language Processing library

by swisscom Python Version: Current License: Apache-2.0

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

kandi X-RAY | ai-research-keyphrase-extraction Summary

ai-research-keyphrase-extraction is a Python library typically used in Artificial Intelligence, Natural Language Processing applications. ai-research-keyphrase-extraction has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.

This is the implementation of the following paper:

Support

Quality

Security

License

Reuse

Support

ai-research-keyphrase-extraction has a low active ecosystem.

It has 322 star(s) with 72 fork(s). There are 31 watchers for this library.

It had no major release in the last 6 months.

There are 5 open issues and 20 have been closed. On average issues are closed in 16 days. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of ai-research-keyphrase-extraction is current.

Quality

ai-research-keyphrase-extraction has 0 bugs and 0 code smells.

Security

ai-research-keyphrase-extraction has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

ai-research-keyphrase-extraction code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

ai-research-keyphrase-extraction is licensed under the Apache-2.0 License. This license is Permissive.

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

Reuse

ai-research-keyphrase-extraction releases are not available. You will need to build from source code and install.

Build file is available. You can build the component from source.

Installation instructions, examples and code snippets are available.

ai-research-keyphrase-extraction saves you 160 person hours of effort in developing the same functionality from scratch.

It has 398 lines of code, 40 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 ai-research-keyphrase-extraction and discovered the below as its top functions. This is intended to give you an instant insight into ai-research-keyphrase-extraction implemented functionality, and help decide if they suit your requirements.

Embeddings for a text object
Implementation of MM algorithm
Return a list of aliases that have a given threshold
Extracts a document embedding from the input text
Extract sentence embedding for a document
Computes the maximum norm of an array
Extract a list of candidates from text_obj
Extract keyphrase from text
Extract embedding for a document
Get grammar
Return unique n - gram candidates
Extract key phrase from text_obj
Embedding key phrase
Tag a list of POS tags
Parse text tag
Tag a POS - tagged text file
Extract candidate tokens from the sentence
Determine if a tagged token is a candidate
Load a PosTagger core
Load an embedding distribution

Get all kandi verified functions for this library.

ai-research-keyphrase-extraction Key Features

No Key Features are available at this moment for ai-research-keyphrase-extraction.

ai-research-keyphrase-extraction Examples and Code Snippets

No Code Snippets are available at this moment for ai-research-keyphrase-extraction.

Community Discussions

Trending Discussions on ai-research-keyphrase-extraction

Access server running on docker container

QUESTION

Access server running on docker container

Asked 2020-Oct-07 at 08:08

I am running the StanfordCoreNLP server through my docker container. Now I want to access it through my python script.

Github repo I'm trying to run: https://github.com/swisscom/ai-research-keyphrase-extraction

I ran the command which gave me the following output:

...

ANSWER

Answered 2020-Oct-07 at 08:08

As seen in the log, your service is listening to port 9000 inside the container. However, from outside you need further information to be able to access it. Two pieces of information that you need:

The IP address of the container
The external port that docker exports this 9000 to the outside (by default docker does not export locally open ports).

To get the IP address you need to use docker inspect, for example via

Source https://stackoverflow.com/questions/64238613

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

Vulnerabilities

No vulnerabilities reported

Install ai-research-keyphrase-extraction

Download full Stanford CoreNLP Tagger version 3.8.0 http://nlp.stanford.edu/software/stanford-corenlp-full-2018-02-27.zip. Install sent2vec from https://github.com/epfml/sent2vec. After cloning this repository go to the root directory and pip install -r requirements.txt. Launch Stanford Core NLP tagger. Set the paths in config.ini.template.
Download full Stanford CoreNLP Tagger version 3.8.0 http://nlp.stanford.edu/software/stanford-corenlp-full-2018-02-27.zip
Install sent2vec from https://github.com/epfml/sent2vec Clone/Download the directory go to sent2vec directory git checkout f827d014a473aa22b2fef28d9e29211d50808d48 make pip install cython inside the src folder python setup.py build_ext pip install . (In OSX) If the setup.py throws an error (ignore warnings), open setup.py and add '-stdlib=libc++' in the compile_opts list. Download a pre-trained model (see readme of Sent2Vec repo) , for example wiki_bigrams.bin
Install requirements After cloning this repository go to the root directory and pip install -r requirements.txt
Download NLTK data
Launch Stanford Core NLP tagger Open a new terminal Go to the stanford-core-nlp-full directory Run the server java -mx4g -cp "*" edu.stanford.nlp.pipeline.StanfordCoreNLPServer -preload tokenize,ssplit,pos -status_port 9000 -port 9000 -timeout 15000 &
Set the paths in config.ini.template You can leave [STANFORDTAGGER] parameters empty For [STANFORDCORENLPTAGGER] : set host to localhost set port to 9000 For [SENT2VEC]: set your model_path to the pretrained model your_path_to_model/wiki_bigrams.bin (if you choosed wiki_bigrams.bin) rename config.ini.template to config.ini

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 .

Find more information at: