pynlpl | Python library for Natural Language Processing | Natural Language Processing library

by proycon Python Version: 1.2.9 License: GPL-3.0

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

kandi X-RAY | pynlpl Summary

pynlpl is a Python library typically used in Artificial Intelligence, Natural Language Processing applications. pynlpl has no bugs, it has no vulnerabilities, it has build file available, it has a Strong Copyleft License and it has low support. You can install using 'pip install pynlpl' or download it from GitHub, PyPI.

PyNLPl, pronounced as 'pineapple', is a Python library for Natural Language Processing. It contains various modules useful for common, and less common, NLP tasks. PyNLPl can be used for basic tasks such as the extraction of n-grams and frequency lists, and to build simple language model. There are also more complex data types and algorithms. Moreover, there are parsers for file formats common in NLP (e.g. FoLiA/Giza/Moses/ARPA/Timbl/CQL). There are also clients to interface with various NLP specific servers. PyNLPl most notably features a very extensive library for working with FoLiA XML (Format for Linguistic Annotation).

Support

Quality

Security

License

Reuse

Support

pynlpl has a low active ecosystem.

It has 456 star(s) with 65 fork(s). There are 31 watchers for this library.

It had no major release in the last 12 months.

There are 3 open issues and 22 have been closed. On average issues are closed in 15 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of pynlpl is 1.2.9

Quality

pynlpl has 0 bugs and 0 code smells.

Security

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

pynlpl code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

pynlpl is licensed under the GPL-3.0 License. This license is Strong Copyleft.

Strong Copyleft licenses enforce sharing, and you can use them when creating open source projects.

Reuse

pynlpl releases are available to install and integrate.

Deployable package is available in PyPI.

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

pynlpl saves you 10993 person hours of effort in developing the same functionality from scratch.

It has 22285 lines of code, 1348 functions and 59 files.

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

Top functions reviewed by kandi - BETA

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

Parse common arguments
Parse a datetime string
Validate an XML NCName identifier
Set phonetic
Correct a correction
Append a child element
Replace a child element
Check if parent is addable
Declare an annotation
Parse xml node
Adds a child element
Generate test sets from files
Parse a token expression
Get syn ids by lemma
Get a list of Luids by lemma
Return a JSON representation of the element
Process input data
Process sourcewords
Processes a data file
Parse the query
Load the model from a file
Declare an annotation type
Substitute a substitution
Generate a unique ID for this element
Compare two source files
Log a message
Find the spans of the given type

Get all kandi verified functions for this library.

pynlpl Key Features

No Key Features are available at this moment for pynlpl.

pynlpl Examples and Code Snippets

No Code Snippets are available at this moment for pynlpl.

Community Discussions

Trending Discussions on pynlpl

Natural Language Processing Algorithms

QUESTION

Natural Language Processing Algorithms

Asked 2017-Feb-08 at 08:20

I am planning on developing a Natural Language Question System using NLP. I have performed literature study regarding the possible algorithms which are applicable for a NLQ System.

The end-user should, after finishing the tool, be able to ask a question to the system, which on its turn gives an answer in the form of a table of will visualize the answer in the form of a graph.

Furthermore, the answering part is already finished. Programming will happen in Python, using the PyNLPl library.

The main tool can already perform mathematical operations and summarizes the outcome of these operations. The user should be able to ask questions as:

"How were the sales on a rainy day in the month january?"
"What is the amount of ... of the whole of Europe"

This question is not meant to be subjective, as I mentioned before, I did literature study. I made a proper selection of the list of algorithms which I found. And am left with a decision of:

POST, Chunking, Named Entity Extraction
Parsing
Topic Modeling and keyword extraction.

Algorithms per bullet point would be:

Conditional Random Fields - Hidden Markov Model
CKY Algorithm - Earley Algorithm
Latend Dirichlet Allocation

Furthermore, the variables which should be mentioned in the questions, are not independent. Is Naive Bayes in that case also applicable? The chosen algorithm, should outperform the rest of the algorithms and make the best fit.

...

ANSWER

Answered 2017-Feb-08 at 08:20

I have been reading and reading, and found answers to almost all my questions. I am sticking to Early algorithm as it offers a dynamic programming approach (CKY does the same). Both algorithms are chart parsing algorithms.

Earley is a context free -, top-down parsing algorithm. Which makes it a goal driven algorithm. From start symbol down. Furthermore, it is more efficient than the CKY algorithm. Slides of comparison, and explanation: https://www.cs.bgu.ac.il/~michaluz/seminar/CKY1.pdf

Note: Earley and CKY parsing algorithms will only result in a parse tree with which nothing can be done. However, Using e.g. shift-reduce dependency parsing algorithm does not only give the dependencies between verious words in addition to parsing and calidating the sentence structure, these dependencies can also be used for relation extraction in the question. In order to really understand the question.

Source https://stackoverflow.com/questions/41953537

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

Vulnerabilities

No vulnerabilities reported

Install pynlpl

You can install using 'pip install pynlpl' or download it from GitHub, PyPI.
You can use pynlpl 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 .

Find more information at: