hazm - Python library for digesting Persian text.

  •        506

Python library for digesting Persian text. We have also trained tagger and parser models. You may put these models in the resources folder of your project.




Related Projects

OpenNLP - Machine learning based toolkit for the processing of natural language text

  •    Java

The Apache OpenNLP library is a machine learning based toolkit for the processing of natural language text. It supports the most common NLP tasks, such as tokenization, sentence segmentation, part-of-speech tagging, named entity extraction, chunking, parsing, and coreference resolution. These tasks are usually required to build more advanced text processing services. OpenNLP also includes maximum entropy and perceptron based machine learning.

pynlpl - PyNLPl, pronounced as 'pineapple', is a Python library for Natural Language Processing

  •    Python

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 Annotatation). The library is a divided into several packages and modules. It works on Python 2.7, as well as Python 3.

cogcomp-nlp - CogComp's Natural Language Processing libraries and Demos:

  •    Java

This project collects a number of core libraries for Natural Language Processing (NLP) developed by Cognitive Computation Group. Each library contains detailed readme and instructions on how to use it. In addition the javadoc of the whole project is available here.

transformers - 🤗Transformers: State-of-the-art Natural Language Processing for Pytorch, TensorFlow, and JAX

  •    Python

🤗 Transformers provides thousands of pretrained models to perform tasks on texts such as classification, information extraction, question answering, summarization, translation, text generation and more in over 100 languages. Its aim is to make cutting-edge NLP easier to use for everyone. 🤗 Transformers provides APIs to quickly download and use those pretrained models on a given text, fine-tune them on your own datasets and then share them with the community on our model hub. At the same time, each python module defining an architecture is fully standalone and can be modified to enable quick research experiments.

speakeasy - A simple natural language tool written for NodeJS

  •    Javascript

The goal of this project is not to be the next final solution for natural language processing. There are plenty of other projects that do a significantly better job of this. SpeakEasy spawned out of another of my projects, Nodebot, as a method of processing user input to simulate the illusion of intelligence. SpeakEasy's goal is to provide a library for NodeJS to perform simple language processing actions that perform well for 70%-80% of all cases.

nlp_tasks - Natural Language Processing Tasks and References


I've been working on several natural language processing tasks for a long time. One day, I felt like drawing a map of the NLP field where I earn a living. I'm sure I'm not the only person who wants to see at a glance which tasks are in NLP. Reviewed and updated by YJ Choe on Oct. 18, 2017.

spago - Self-contained Machine Learning and Natural Language Processing library in Go

  •    Go

A Machine Learning library written in pure Go designed to support relevant neural architectures in Natural Language Processing. spaGO is self-contained, in that it uses its own lightweight computational graph framework for both training and inference, easy to understand from start to finish.

WikiSQL - A large annotated semantic parsing corpus for developing natural language interfaces.

  •    HTML

A large crowd-sourced dataset for developing natural language interfaces for relational databases. WikiSQL is the dataset released along with our work Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning. Victor Zhong, Caiming Xiong, and Richard Socher. 2017. Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning.

spark-nlp - Natural Language Understanding Library for Apache Spark.

  •    Jupyter

John Snow Labs Spark-NLP is a natural language processing library built on top of Apache Spark ML. It provides simple, performant & accurate NLP annotations for machine learning pipelines, that scale easily in a distributed environment. This library has been uploaded to the spark-packages repository https://spark-packages.org/package/JohnSnowLabs/spark-nlp .

nlp-architect - NLP Architect by Intel AI Lab: Python library for exploring the state-of-the-art deep learning topologies and techniques for natural language processing and natural language understanding

  •    Python

NLP Architect is an open-source Python library for exploring state-of-the-art deep learning topologies and techniques for natural language processing and natural language understanding. It is intended to be a platform for future research and collaboration. Framework documentation on NLP models, algorithms, and modules, and instructions on how to contribute can be found at our main documentation site.

text-analytics-with-python - Learn how to process, classify, cluster, summarize, understand syntax, semantics and sentiment of text data with the power of Python! This repository contains code and datasets used in my book, "Text Analytics with Python" published by Apress/Springer

  •    Python

Derive useful insights from your data using Python. Learn the techniques related to natural language processing and text analytics, and gain the skills to know which technique is best suited to solve a particular problem. A structured and comprehensive approach is followed in this book so that readers with little or no experience do not find themselves overwhelmed. You will start with the basics of natural language and Python and move on to advanced analytical and machine learning concepts. You will look at each technique and algorithm with both a bird's eye view to understand how it can be used as well as with a microscopic view to understand the mathematical concepts and to implement them to solve your own problems.

spaCy - 💫 Industrial-strength Natural Language Processing (NLP) with Python and Cython

  •    Python

spaCy is a library for advanced Natural Language Processing in Python and Cython. It's built on the very latest research, and was designed from day one to be used in real products. spaCy comes with pre-trained statistical models and word vectors, and currently supports tokenization for 20+ languages. It features the fastest syntactic parser in the world, convolutional neural network models for tagging, parsing and named entity recognition and easy deep learning integration. It's commercial open-source software, released under the MIT license. 💫 Version 2.0 out now! Check out the new features here.

Deep-Learning-NLP - :satellite: Organized Resources for Deep Learning in Natural Language Processing

  •    Python

The purpose of this project is to introduce a shortcut to developers and researcher for finding useful resources about Deep Learning for Natural Language Processing. There are different motivations for this open source project.

natural-language-processing - Resources for "Natural Language Processing" Coursera course.

  •    Jupyter

Resources for "Natural Language Processing" Coursera course.

delta - DELTA is a deep learning based natural language and speech processing platform.

  •    Python

DELTA is a deep learning based end-to-end natural language and speech processing platform. DELTA aims to provide easy and fast experiences for using, deploying, and developing natural language processing and speech models for both academia and industry use cases. DELTA is mainly implemented using TensorFlow and Python 3. For details of DELTA, please refer to this paper.

nlp-with-ruby - Practical Natural Language Processing done in Ruby.

  •    Ruby

This curated list comprises awesome resources, libraries, information sources about computational processing of texts in human languages with the Ruby programming language. That field is often referred to as NLP, Computational Linguistics, HLT (Human Language Technology) and can be brought in conjunction with Artificial Intelligence, Machine Learning, Information Retrieval, Text Mining, Knowledge Extraction and other related disciplines. This list comes from our day to day work on Language Models and NLP Tools. Read why this list is awesome. Our FAQ describes the important decisions and useful answers you may be interested in.

treat - Natural language processing framework for Ruby.

  •    Ruby

Treat is a toolkit for natural language processing and computational linguistics in Ruby. The Treat project aims to build a language- and algorithm- agnostic NLP framework for Ruby with support for tasks such as document retrieval, text chunking, segmentation and tokenization, natural language parsing, part-of-speech tagging, keyword extraction and named entity recognition. Learn more by taking a quick tour or by reading the manual. I am actively seeking developers that can help maintain and expand this project. You can find a list of ideas for contributing to the project here.

gt-nlp-class - Course materials for Georgia Tech CS 4650 and 7650, "Natural Language"

  •    TeX

This course gives an overview of modern data-driven techniques for natural language processing. The course moves from shallow bag-of-words models to richer structural representations of how words interact to create meaning. At each level, we will discuss the salient linguistic phemonena and most successful computational models. Along the way we will cover machine learning techniques which are especially relevant to natural language processing. Readings will be drawn mainly from my notes. Additional readings may be assigned from published papers, blogposts, and tutorials.

NLP-progress - Repository to track the progress in Natural Language Processing (NLP), including the datasets and the current state-of-the-art for the most common NLP tasks

  •    HTML

This document aims to track the progress in Natural Language Processing (NLP) and give an overview of the state-of-the-art (SOTA) across the most common NLP tasks and their corresponding datasets. It aims to cover both traditional and core NLP tasks such as dependency parsing and part-of-speech tagging as well as more recent ones such as reading comprehension and natural language inference. The main objective is to provide the reader with a quick overview of benchmark datasets and the state-of-the-art for their task of interest, which serves as a stepping stone for further research. To this end, if there is a place where results for a task are already published and regularly maintained, such as a public leaderboard, the reader will be pointed there.

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