Open Source Natural Language Processing (NLP) Tools
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Natural Language Processing (NLP) Tools
View 188 business solutionsBrowse free open source Natural Language Processing (NLP) tools and projects below. Use the toggles on the left to filter open source Natural Language Processing (NLP) tools by OS, license, language, programming language, and project status.
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Gen AI apps are built with MongoDB AtlasMongoDB Atlas provides built-in vector search and a flexible document model so developers can build, scale, and run gen AI apps without stitching together multiple databases. From LLM integration to semantic search, Atlas simplifies your AI architecture—and it’s free to get started.
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Orchestrate Your AI Agents with ZenflowZenflow orchestrates AI agents like a real engineering system. With parallel execution, spec-driven workflows, and deep multi-repo understanding, agents plan, implement, test, and verify end-to-end. Upgrade to AI workflows that work the way your team does.
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MeCab is a fast and customizable Japanese morphological analyzer. MeCab is designed for generic purpose and applied to variety of NLP tasks, such as Kana-Kanji conversion. MeCab provides parameter estimation functionalities based on CRFs and HMM
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Virastyar
Virastyar is an spell checker for low-resource languages
Virastyar is a free and open-source (FOSS) spell checker. It stands upon the shoulders of many free/libre/open-source (FLOSS) libraries developed for processing low-resource languages, especially Persian and RTL languages Publications: Kashefi, O., Nasri, M., & Kanani, K. (2010). Towards Automatic Persian Spell Checking. SCICT. Kashefi, O., Sharifi, M., & Minaie, B. (2013). A novel string distance metric for ranking Persian respelling suggestions. Natural Language Engineering, 19(2), 259-284. Rasooli, M. S., Kahefi, O., & Minaei-Bidgoli, B. (2011). Effect of adaptive spell checking in Persian. In NLP-KE Contributors: Omid Kashefi Azadeh Zamanifar Masoumeh Mashaiekhi Meisam Pourafzal Reza Refaei Mohammad Hedayati Kamiar Kanani Mehrdad Senobari Sina Iravanin Mohammad Sadegh Rasooli Mohsen Hoseinalizadeh Mitra Nasri Alireza Dehlaghi Fatemeh Ahmadi Neda PourMorteza -
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OpenVINO
OpenVINO™ Toolkit repository
OpenVINO™ is an open-source toolkit for optimizing and deploying AI inference. Boost deep learning performance in computer vision, automatic speech recognition, natural language processing and other common tasks. Use models trained with popular frameworks like TensorFlow, PyTorch and more. Reduce resource demands and efficiently deploy on a range of Intel® platforms from edge to cloud. This open-source version includes several components: namely Model Optimizer, OpenVINO™ Runtime, Post-Training Optimization Tool, as well as CPU, GPU, MYRIAD, multi device and heterogeneous plugins to accelerate deep learning inferencing on Intel® CPUs and Intel® Processor Graphics. It supports pre-trained models from the Open Model Zoo, along with 100+ open source and public models in popular formats such as TensorFlow, ONNX, PaddlePaddle, MXNet, Caffe, Kaldi. -
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Diffgram
Training data (data labeling, annotation, workflow) for all data types
From ingesting data to exploring it, annotating it, and managing workflows. Diffgram is a single application that will improve your data labeling and bring all aspects of training data under a single roof. Diffgram is world’s first truly open source training data platform that focuses on giving its users an unlimited experience. This is aimed to reduce your data labeling bills and increase your Training Data Quality. Training Data is the art of supervising machines through data. This includes the activities of annotation, which produces structured data; ready to be consumed by a machine learning model. Annotation is required because raw media is considered to be unstructured and not usable without it. That’s why training data is required for many modern machine learning use cases including computer vision, natural language processing and speech recognition. -
Auth0 for AI Agents now in GAConnect your AI agents to apps and data more securely, give users control over the actions AI agents can perform and the data they can access, and enable human confirmation for critical agent actions.
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gse
Go efficient multilingual NLP and text segmentation
Go efficient multilingual NLP and text segmentation; support English, Chinese, Japanese and others. Gse is implements jieba by golang, and try add NLP support and more feature. Support common, search engine, full mode, precise mode and HMM mode multiple word segmentation modes. Support user and embed dictionary, Part-of-speech/POS tagging, analyze segment info, stop and trim words. Support multilingual: English, Chinese, Japanese and others. Support Traditional Chinese. Support HMM cut text use Viterbi algorithm. Support NLP by TensorFlow (in work). Named Entity Recognition (in work). Supports with elastic search and bleve. run JSON RPC service. -
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ChatGLM.cpp
C++ implementation of ChatGLM-6B & ChatGLM2-6B & ChatGLM3 & GLM4(V)
ChatGLM.cpp is a C++ implementation of the ChatGLM-6B model, enabling efficient local inference without requiring a Python environment. It is optimized for running on consumer hardware. -
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VADER
Lexicon and rule-based sentiment analysis tool
VADER (Valence Aware Dictionary and sEntiment Reasoner) is a lexicon and rule-based sentiment analysis tool designed for analyzing the sentiment of text, particularly in social media and short text formats. It is optimized for quick and accurate analysis of positive, negative, and neutral sentiments. -
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spaCy
Industrial-strength Natural Language Processing (NLP)
spaCy is a library built on the very latest research for advanced Natural Language Processing (NLP) in Python and Cython. Since its inception it was designed to be used for real world applications-- for building real products and gathering real insights. It comes with pretrained statistical models and word vectors, convolutional neural network models, easy deep learning integration and so much more. spaCy is the fastest syntactic parser in the world according to independent benchmarks, with an accuracy within 1% of the best available. It's blazing fast, easy to install and comes with a simple and productive API. -
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Botpress
Dev tools to reliably understand text and automate conversations
We make building chatbots much easier for developers. We have put together the boilerplate code and infrastructure you need to get a chatbot up and running. We propose you a complete dev-friendly platform that ships with all the tools you need to build, deploy and manage production-grade chatbots in record time. Built-in Natural Language Processing tasks such as intent recognition, spell checking, entity extraction, and slot tagging (and many others). A visual conversation studio to design multi-turn conversations and workflows. An emulator & a debugger to simulate conversations and debug your chatbot. Support for popular messaging channels like Slack, Telegram, MS Teams, Facebook Messenger, and an embeddable web chat. An SDK and code editor to extend the capabilities. Post-deployment tools like analytics dashboards, human handoff and more. -
Total Network Visibility for Network Engineers and IT ManagersThis means every device on your network, and every interface on every device is automatically analyzed for performance, errors, QoS, and configuration.
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ModelScope
Bring the notion of Model-as-a-Service to life
ModelScope is built upon the notion of “Model-as-a-Service” (MaaS). It seeks to bring together most advanced machine learning models from the AI community, and streamlines the process of leveraging AI models in real-world applications. The core ModelScope library open-sourced in this repository provides the interfaces and implementations that allow developers to perform model inference, training and evaluation. In particular, with rich layers of API abstraction, the ModelScope library offers unified experience to explore state-of-the-art models spanning across domains such as CV, NLP, Speech, Multi-Modality, and Scientific-computation. Model contributors of different areas can integrate models into the ModelScope ecosystem through the layered APIs, allowing easy and unified access to their models. Once integrated, model inference, fine-tuning, and evaluations can be done with only a few lines of code. -
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Ciphey
Decrypt encryptions without knowing the key or cipher
Fully automated decryption/decoding/cracking tool using natural language processing & artificial intelligence, along with some common sense. You don't know, you just know it's possibly encrypted. Ciphey will figure it out for you. Ciphey can solve most things in 3 seconds or less. Ciphey aims to be a tool to automate a lot of decryptions & decodings such as multiple base encodings, classical ciphers, hashes or more advanced cryptography. If you don't know much about cryptography, or you want to quickly check the ciphertext before working on it yourself, Ciphey is for you. The technical part. Ciphey uses a custom-built artificial intelligence module (AuSearch) with a Cipher Detection Interface to approximate what something is encrypted with. And then a custom-built, customizable natural language processing Language Checker Interface, which can detect when the given text becomes plaintext. -
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TextBlob
TextBlob is a Python library for processing textual data
Simple, Pythonic, text processing, Sentiment analysis, part-of-speech tagging, noun phrase extraction, translation, and more. It provides a simple API for diving into common natural language processing (NLP) tasks such as part-of-speech tagging, noun phrase extraction, sentiment analysis, classification, translation, and more. TextBlob stands on the giant shoulders of NLTK and pattern, and plays nicely with both. Supports word inflection (pluralization and singularization) and lemmatization, as well as spelling correction. Add new models or languages through extensions. Also, it comes with a WordNet integration. If you only intend to use TextBlob’s default models (no model overrides), you can pass the lite argument. This downloads only those corpora needed for basic functionality. TextBlob is also available as a conda package. -
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API-for-Open-LLM
Openai style api for open large language models
API-for-Open-LLM is a lightweight API server designed for deploying and serving open large language models (LLMs), offering a simple way to integrate LLMs into applications. -
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BotSharp
AI Multi-Agent Framework in .NET
Conversation as a platform (CaaP) is the future, so it's perfect that we're already offering the whole toolkits to our .NET developers using the BotSharp AI BOT Platform Builder to build a CaaP. It opens up as much learning power as possible for your own robots and precisely control every step of the AI processing pipeline. BotSharp is an open source machine learning framework for AI Bot platform builder. This project involves natural language understanding, computer vision and audio processing technologies, and aims to promote the development and application of intelligent robot assistants in information systems. Out-of-the-box machine learning algorithms allow ordinary programmers to develop artificial intelligence applications faster and easier. It's written in C# running on .Net Core that is full cross-platform framework. C# is a enterprise-grade programming language which is widely used to code business logic in information management-related system. -
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Open Interpreter
A natural language interface for computers
Open Interpreter is an open-source tool that provides a natural-language interface for interacting with your computer. It lets large language models (LLMs) run code locally (Python, JavaScript, shell, etc.), enabling you to ask your computer to do tasks like data analysis, file manipulation, browsing, etc. in human terms (“chat with your computer”), with safeguards. Runs locally or via configured remote LLM servers/inference backends, giving flexibility to use models you trust or have locally. It prompts you to approve code before executing, and supports both online LLM models and local inference servers. It seeks to combine convenience (like ChatGPT’s code interpreter) with control and flexibility by running on your own machine. -
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STORM
An LLM-powered knowledge curation system that researches topics
STORM is an open-source virtual assistant framework developed by Stanford's OVAL lab. It is designed for creating natural language interfaces and assistants that can interact with APIs, databases, and services in a modular way. -
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compromise
Modest natural-language processing
Language is complicated and there's a gazillion words. Compromise is a javascript library that interprets and pre-parses text and makes some reasonable decisions so things are way easier. Compromise tries its best to parse text. it is small, quick, and often good-enough. It is not as smart as you'd think. Conjugate and negate verbs in any tense. Play between plural, singular and possessive forms. Interpret plain-text numbers. Handle implicit terms. Use it on the client-side or as an es-module. compromise is 180kb (minified). It's pretty fast. It can run on keypress. It works mainly by conjugating all forms of a basic word list. Decide how words get interpreted or make heavier changes with a compromise-plugin. Parse text without running POS-tagging. Pre-parse any match statements for faster lookups. It is not the most accurate, or clever nlp library, but found its niche as an easy, small library that can run everywhere. -
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libpostal
A C library for parsing/normalizing street addresses around the world
A C library for parsing/normalizing street addresses around the world. Powered by statistical NLP and open geo data. libpostal is a C library for parsing/normalizing street addresses around the world using statistical NLP and open data. The goal of this project is to understand location-based strings in every language, everywhere. Addresses and the locations they represent are essential for any application dealing with maps (place search, transportation, on-demand/delivery services, check-ins, reviews). Yet even the simplest addresses are packed with local conventions, abbreviations and context, making them difficult to index/query effectively with traditional full-text search engines. This library helps convert the free-form addresses that humans use into clean normalized forms suitable for machine comparison and full-text indexing. Though libpostal is not itself a full geocoder, it can be used as a preprocessing step to make any geocoding application smarter, and simpler. -
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OpenNLP provides the organizational structure for coordinating several different projects which approach some aspect of Natural Language Processing. OpenNLP also defines a set of Java interfaces and implements some basic infrastructure for NLP compon
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BEIR
A Heterogeneous Benchmark for Information Retrieval
BEIR is a benchmark framework for evaluating information retrieval models across various datasets and tasks, including document ranking and question answering. -
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Botonic
Build chatbots and conversational experiences using React
Botonic is a full-stack Javascript framework to create chatbots and modern conversational apps that work on multiple platforms, web, mobile and messaging apps (Messenger, Whatsapp, Telegram, etc). Building modern applications on top of messaging apps like Whatsapp or Messenger is much more than creating simple text-based chatbots. Botonic is a full-stack serverless framework that combines the power of React and Tensorflow.js to create amazing experiences at the intersection of text and graphical interfaces. With Botonic you can focus on creating the best conversational experience for your users instead of dealing with different messaging APIs, AI/NLP complexity or managing and scaling infrastructure. It also comes with a battery of plugins so you can easily integrate popular services into your project. -
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Colossal-AI
Making large AI models cheaper, faster and more accessible
The Transformer architecture has improved the performance of deep learning models in domains such as Computer Vision and Natural Language Processing. Together with better performance come larger model sizes. This imposes challenges to the memory wall of the current accelerator hardware such as GPU. It is never ideal to train large models such as Vision Transformer, BERT, and GPT on a single GPU or a single machine. There is an urgent demand to train models in a distributed environment. However, distributed training, especially model parallelism, often requires domain expertise in computer systems and architecture. It remains a challenge for AI researchers to implement complex distributed training solutions for their models. Colossal-AI provides a collection of parallel components for you. We aim to support you to write your distributed deep learning models just like how you write your model on your laptop. -
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DeepSparse
Sparsity-aware deep learning inference runtime for CPUs
A sparsity-aware enterprise inferencing system for AI models on CPUs. Maximize your CPU infrastructure with DeepSparse to run performant computer vision (CV), natural language processing (NLP), and large language models (LLMs). -
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Dragonfire
The open-source virtual assistant for Ubuntu based Linux distributions
Dragonfire is the open-source virtual assistant project for Ubuntu-based Linux distributions. Her main objective is to serve as a command and control interface to the helmet user. So that you will be able to give orders just by using your voice commands and your eye movements. That makes the helmet handsfree. We are planning to ship Dragonfire as a preinstalled software package on DragonOS Linux Distribution. DragonOS will be a Linux distribution specially designed for the helmet. It will contain various software packages for controlling the helmet. It will be the first of its kind. Dragonfire uses Mozilla DeepSpeech to understand your voice commands and Festival Speech Synthesis System to handle text-to-speech tasks. -
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HanLP
Han Language Processing
HanLP is a multilingual Natural Language Processing (NLP) library composed of a series of models and algorithms. Built on TensorFlow 2.0, it was designed to advance state-of-the-art deep learning techniques and popularize the application of natural language processing in both academia and industry. HanLP is capable of lexical analysis (Chinese word segmentation, part-of-speech tagging, named entity recognition), syntax analysis, text classification, and sentiment analysis. It comes with pretrained models for numerous languages including Chinese and English. It offers efficient performance, clear structure and customizable features, with plenty more amazing features to look forward to on the roadmap.
Open Source Natural Language Processing (NLP) Tools Guide
Open source natural language processing (NLP) tools are software applications designed to help users analyze, interpret, and understand text. They are usually developed as an open source project by a community of developers who collaborate together to develop the application.Open source NLP tools often utilize sophisticated algorithms and techniques such as machine learning, deep learning, and natural language understanding to provide insights into text data. These insights can be used for many purposes such as sentiment analysis, topic classification, automatic summarization, entity extraction, and question answering. In addition to being open source projects, these tools are free from cost which is attractive for researchers and business owners who don't have the budget for expensive commercial NLP software solutions. With their flexibility and affordability in mind many businesses have adopted open source NLP tools for data analysis purposes such as customer service chatbot development or social media monitoring projects. Open source NLP tools can be deployed on-premises or in the cloud making them even more versatile when it comes to using them in production systems.
Features of Open Source Natural Language Processing (NLP) Tools
- Tokenization: Process of splitting a sentence into its individual words or phrases, known as tokens.
- Part-Of-Speech Tagging: A process that assigns part-of-speech tags (nouns, verbs, adjectives etc.) to each token in a sentence.
- Named Entity Recognition: A process for detecting and classifying named entities (people, places, organizations etc.) from unstructured text.
- Syntactic Parsing: Process of segmenting text into smaller pieces to determine the meaning and structure of a sentence.
- Semantic Analysis: A process for extracting the underlying meaning behind a set of words by connecting them with relevant context or facts.
- Sentiment Analysis: Process used to identify subjective opinions expressed in text and classify it as either positive or negative.
- Summarization & Text Simplification: Refers to techniques used to produce shorter versions of texts while maintaining the key information contained within them.
- Machine Translation & Language Identification: Natural language processing tools used to detect source language and automatically translate it into another target language.
Different Types of Open Source Natural Language Processing (NLP) Tools
- GATE (General Architecture for Text Engineering): GATE is an open-source platform for performing NLP tasks such as text mining and information extraction. It provides modular components that can be used to build more complex applications.
- Stanford CoreNLP: Stanford CoreNLP is a suite of tools for natural language processing of English, Chinese, French, Spanish and other languages. It includes a set of core Java libraries and command line tools which allow developers to create custom NLP pipelines.
- NLTK (Natural Language ToolKit): NLTK is an open source library used to build Python programs that can analyze natural language. It provides interfaces to more than 50 corpora and lexical resources, along with wrappers for over 50 NLP applications.
- spaCy: SpaCy is a library for advanced NLP in Python designed specifically for production use on large datasets. It allows developers to quickly create systems that can process large volumes of text accurately and efficiently using its efficient algorithms and Pipelines-based architecture.
- OpenNLP: OpenNLP is an Apache-licensed open source toolkit developed by the Apache Software Foundation for the processing of human language data like tokenization, segmentation, categorization, parsing etc., written in Java programming language.
- UIMA (Unstructured Information Management Architecture): UIMA is an open source framework developed by IBM Research specifically designed to enable development of applications which search unstructured content and extract information from it like annotations, relationships etc., through annotators written in Java or C++ programming language.
Open Source Natural Language Processing (NLP) Tools Advantages
- Cost: Using open source NLP tools is often free, or much more cost effective than expensive licensed software. This makes it an ideal choice for businesses who have smaller budgets, as well as individuals and researchers.
- Efficiency: Open source NLP tools are available immediately, with no need to purchase or wait for a license. This makes them great when you need results quickly.
- Flexibility: Open source NLP tools are often very customizable and can be adapted to many different tasks. This provides flexibility in using the tool for a variety of needs.
- Portability: Since they are open source, these tools can be used on any operating system without the need to install additional software. They can also easily be shared and distributed among colleagues or students in a class setting with minimal effort.
- Security & Privacy: Many open source solutions guarantee that your code is not only secure but private too, meaning that no one else will have access to confidential data or research results from your projects unless you choose to share them publicly.
- Community Support & Development: The advantage of having an active community behind their development ensures that these NLP solutions stay up-to-date and keep improving rapidly with the regular updates provided by the community developers addressing bugs and adding new features. Additionally, having so many people contributing allows users of open source tools to get help faster if they face a problem when using the tool set.
What Types of Users Use Open Source Natural Language Processing (NLP) Tools?
- Researchers: Scientists and academics who use open source NLP tools to study language, its meaning, and its context.
- Educators: Those who teach students about the basics of natural language processing as a part of their coursework.
- Data Analysts: Analysts leverage open source NLP tools to extract insights from datasets or text-based sources.
- Application Developers: Software engineers and application developers who use open source NLP libraries for tasks like creating chatbots or building speech recognition software.
- Machine Learning Engineers: Professionals who develop machine learning models that utilize natural language processing techniques.
- Business Analytics Teams: Companies often have analytics teams that apply NLP techniques to their customer data in order to better understand customer behavior and preferences.
- Webmasters: Webmasters can use open source NLP libraries to automatically generate content or monitor webpages for certain key words or phrases.
- Journalists & Content Creators: Journalists, bloggers, copywriters, etc., commonly use open source NLP tools to organize notes, generate content outlines and edit drafts more efficiently than before.
How Much Do Open Source Natural Language Processing (NLP) Tools Cost?
Open source natural language processing (NLP) tools are typically free to use. As open source software, they are developed and maintained by a community of volunteers who donate their time and energy to create quality code that can be used by anyone across the world. This means that you don’t have to pay a cent for creating sophisticated NLP models or applications using open source NLP tools.
With an increasing number of open source resources available today, you can find various kinds of data sets, tools and frameworks for building your own classifiers for sentiment analysis, text summarization or even machine translation systems. Some of these resources include popular libraries like Natural Language Toolkit (NLTK), Python-based TensorFlow library, OpenNLP from Apache Software Foundation and SpaCy – an industrial-strength natural language understanding library in Python.
These libraries come with extensive documentation on how to use them as well as detailed instructions on how to implement particular tasks — such as text classification or information extraction — leveraging the power of machine learning algorithms. With only basic programming knowledge required, one can create complex tools or extend existing ones with just a few lines of code. Thus there is no need for costly licenses related to closed-source software when working with free and open source NLP tools.
What Software Do Open Source Natural Language Processing (NLP) Tools Integrate With?
Open source natural language processing (NLP) tools can be integrated with a variety of software, including chatbot development platforms, analytic and business intelligence platforms, enterprise search solutions, automation and workflow management systems, customer support software, voice recognition technologies, and more. Many of these types of software provide APIs or other integration services that allow developers to quickly connect their NLP tools to other applications. By connecting open source NLP tools to other applications through these interfaces, users can leverage the power of NLP for use cases such as automatically analyzing customer data for sentiment analysis or creating virtual agents using natural language commands.
What Are the Trends Relating to Open Source Natural Language Processing (NLP) Tools?
- Open source NLP tools are becoming increasingly popular due to their flexibility and affordability.
- Developers have access to a wide range of software libraries, from which they can pick the best fit for their projects.
- Deep learning algorithms have been incorporated into many open source NLP tools, resulting in more accurate language processing.
- Open source frameworks such as spaCy, NLTK, and Gensim offer developers the opportunity to customize models and hyperparameters.
- Open source NLP tools make it easier for developers to integrate pre-trained models into their applications.
- These tools are being used more frequently in various applications such as chatbot development, text summarization, sentiment analysis, natural language understanding, etc.
- Many open source libraries also provide support for multiple languages, making them accessible to a wider audience.
- There has been increased focus on open source efforts in the industry, with companies investing resources in developing new NLP tools and services.
- Open source NLP tools are becoming more user-friendly and accessible over time, allowing more developers to benefit from them.
How Users Can Get Started With Open Source Natural Language Processing (NLP) Tools
Getting started with using open source Natural Language Processing (NLP) projects is easier than ever now that there are a wide range of popular and powerful projects available.
The first step in getting up to speed on open source NLP tools is to familiarize yourself with the most popular frameworks, libraries, and packages available. There are dozens of options out there, including spaCy, NLTK, OpenNLP, NLU-Evaluation Framework (NEF), Stanford CoreNLP, Gensim, AllenNLP, and HuggingFace Transformers. Different projects focus on different tasks (e.g., tokenization), so you should consider which project is best suited for your particular needs. Once you’ve chosen a project or framework that fits your requirements best it's time to get started.
Fortunately tutorials for many of these packages are commonly updated as new versions come out or bugs have been fixed. A great place to start if you're new to using open source NLP tools is training courses such as Natural Language Processing with Python from Coursera or Udacity's Intro to Natural Language Processing course. These courses will help you understand the basics of NLP concepts and algorithms as well as provide an overview of the various tools and packages available for use in developing solutions for natural language processing tasks.
Once you've completed any necessary training online or elsewhere it's time to dig deeper into each package and library that interests you most. Each project often has its own official website containing extensive documentation explaining not only how set up the software but also how certain features work exactly under different settings etc.. Github repos can often provide more insights into an algorithm’s capabilities by providing examples written by users who may have already solved a problem similar to yours before. Lastly don't forget about local user groups where passionate people eager to help newcomers meet in person share their experiences while demystifying some technical hurdles along the way.
