Open Source Natural Language Processing (NLP) Tools
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Natural Language Processing (NLP) Tools
View 26 business solutionsBrowse free open source Natural Language Processing (NLP) tools and projects for Windows and Linux 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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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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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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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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Stanza
Stanford NLP Python library for many human languages
Stanza is a collection of accurate and efficient tools for the linguistic analysis of many human languages. Starting from raw text to syntactic analysis and entity recognition, Stanza brings state-of-the-art NLP models to languages of your choosing. Stanza is a Python natural language analysis package. It contains tools, which can be used in a pipeline, to convert a string containing human language text into lists of sentences and words, to generate base forms of those words, their parts of speech and morphological features, to give a syntactic structure dependency parse, and to recognize named entities. The toolkit is designed to be parallel among more than 70 languages, using the Universal Dependencies formalism. Stanza is built with highly accurate neural network components that also enable efficient training and evaluation with your own annotated data. -
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txtai
Build AI-powered semantic search applications
txtai executes machine-learning workflows to transform data and build AI-powered semantic search applications. Traditional search systems use keywords to find data. Semantic search applications have an understanding of natural language and identify results that have the same meaning, not necessarily the same keywords. Backed by state-of-the-art machine learning models, data is transformed into vector representations for search (also known as embeddings). Innovation is happening at a rapid pace, models can understand concepts in documents, audio, images and more. Machine-learning pipelines to run extractive question-answering, zero-shot labeling, transcription, translation, summarization and text extraction. Cloud-native architecture that scales out with container orchestration systems (e.g. Kubernetes). Applications range from similarity search to complex NLP-driven data extractions to generate structured databases. The following applications are powered by txtai. -
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Transformers4Rec
Transformers4Rec is a flexible and efficient library
Transformers4Rec is an advanced recommendation system library that leverages Transformer models for sequential and session-based recommendations. The library works as a bridge between natural language processing (NLP) and recommender systems (RecSys) by integrating with one of the most popular NLP frameworks, Hugging Face Transformers (HF). Transformers4Rec makes state-of-the-art transformer architectures available for RecSys researchers and industry practitioners. Traditional recommendation algorithms usually ignore the temporal dynamics and the sequence of interactions when trying to model user behavior. Generally, the next user interaction is related to the sequence of the user's previous choices. In some cases, it might be a repeated purchase or song play. User interests can also suffer from interest drift because preferences can change over time. Those challenges are addressed by the sequential recommendation task. -
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Botkit
Tool for building chat bots, apps and custom integrations
An open source developer tool for building chat bots, apps and custom integrations for major messaging platforms. Part of the Microsoft Bot Framework. We love bots, and want to make them easy and fun to build! Include Botkit into your Node application and boot up a controller that will define your bot's behaviors. In this case, we're setting up a bot to use with the Bot Framework Emulator. Tell the bot to listen for users saying "hello," and use `bot.reply` to send an immediate response. Start a conversation, then queue up multiple messages to send, including a prompt sent using `convo.ask()` which allows your bot to capture user input and use it. Botkit is just one part of a bigger set of developer tools and SDKs that encompass the Microsoft Bot Framework. The Bot Framework SDK provides the base upon which Botkit is built. It is available in multiple programming languages! -
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amazon-connect-wisdomjs
Gives you the power to build your own Wisdom widget
Amazon Connect Wisdom, a feature of Amazon Connect, delivers agents the information they need, reducing the time spent searching for answers. Today, knowledge articles, wikis, and FAQs are spread across separate repositories. Agents lose a lot of time trying to navigate all those different sources of information, and in the meantime, the customer waits for an answer. Amazon Connect Wisdom connects relevant knowledge repositories with built-in connectors for third-party applications like Salesforce and ServiceNow, as well as internal wikis, FAQ stores, and file shares. With Wisdom, agents can search across connected repositories to find answers and quickly resolve customer issues. In addition, Wisdom uses real-time speech analytics and natural language processing (NLP) from Contact Lens for Amazon Connect to detect customer issues during calls, and then provide agents with recommendations and answers. Wisdom provides faster issue resolution and improved customer satisfaction. -
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DeepLearning
Deep Learning (Flower Book) mathematical derivation
" Deep Learning " is the only comprehensive book in the field of deep learning. The full name is also called the Deep Learning AI Bible (Deep Learning) . It is edited by three world-renowned experts, Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Includes linear algebra, probability theory, information theory, numerical optimization, and related content in machine learning. At the same time, it also introduces deep learning techniques used by practitioners in the industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling and practical methods, and investigates topics such as natural language processing, Applications in speech recognition, computer vision, online recommender systems, bioinformatics, and video games. Finally, the Deep Learning book provides research directions covering theoretical topics including linear factor models, autoencoders, representation learning, structured probabilistic models, etc. -
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AWS Toolkit for Visual Studio Code
Local Lambda debug, CodeWhisperer, SAM/CFN syntax, etc.
The AWS Toolkit extension for Visual Studio Code enables you to interact with Amazon Web Services (AWS). Try the AWS Code Sample Catalog to start coding with the AWS SDK. The AWS Explorer provides access to the AWS services that you can work with when using the Toolkit. To see the AWS Explorer, choose the AWS icon in the Activity bar. The Developer Tools panel is a section for developer-focused tooling curated for working in an IDE. The Developer Tools panel can be found underneath the AWS Explorer when the AWS icon is selected in the Activity bar. The AWS CDK Explorer enables you to work with AWS Cloud Development Kit (CDK) applications. It shows a top-level view of your CDK applications that have been synthesized in your workspace. Amazon CodeWhisperer provides inline code suggestions using machine learning and natural language processing on the contents of your current file. Supported languages include Java, Python and Javascript. -
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NNCF
Neural Network Compression Framework for enhanced OpenVINO
NNCF (Neural Network Compression Framework) is an optimization toolkit for deep learning models, designed to apply quantization, pruning, and other techniques to improve inference efficiency. -
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Common Resource Grep - crgrep
Common Resource Grep
CRGREP searches for matching text in databases, various document formats, archives and other difficult to access resources. A command line tool for name and content text matching in database tables, plain files, MS Office documents, PDF, archives, MP3 audio, image meta-data, scanned documents, maven dependencies and web resources. CRGREP will search resources within resources of any arbitrary combination or depth, so text within a document within a zip archive, and so on. Here you will find binary downloads and discussion (https://sourceforge.net/p/crgrep/discussion/) . The actual development and issue tracking can be found here: https://bitbucket.org/cryanfuse/crgrep -
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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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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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DeepPavlov
A library for deep learning end-to-end dialog systems and chatbots
DeepPavlov makes it easy for beginners and experts to create dialogue systems. The best place to start is with user-friendly tutorials. They provide quick and convenient introduction on how to use DeepPavlov with complete, end-to-end examples. No installation needed. Guides explain the concepts and components of DeepPavlov. Follow step-by-step instructions to install, configure and extend DeepPavlov framework for your use case. DeepPavlov is an open-source framework for chatbots and virtual assistants development. It has comprehensive and flexible tools that let developers and NLP researchers create production-ready conversational skills and complex multi-skill conversational assistants. Use BERT and other state-of-the-art deep learning models to solve classification, NER, Q&A and other NLP tasks. DeepPavlov Agent allows building industrial solutions with multi-skill integration via API services. -
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Graph4NLP
Graph4nlp is the library for the easy use of Graph Neural Networks
Graph4NLP is an easy-to-use library for R&D at the intersection of Deep Learning on Graphs and Natural Language Processing (i.e., DLG4NLP). It provides both full implementations of state-of-the-art models for data scientists and also flexible interfaces to build customized models for researchers and developers with whole-pipeline support. Built upon highly-optimized runtime libraries including DGL , Graph4NLP has both high running efficiency and great extensibility. The architecture of Graph4NLP is shown in the following figure, where boxes with dashed lines represent the features under development. Graph4NLP consists of four different layers: 1) Data Layer, 2) Module Layer, 3) Model Layer, and 4) Application Layer. Graph4nlp aims to make it incredibly easy to use GNNs in NLP tasks (check out Graph4NLP Documentation). -
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PyText
A natural language modeling framework based on PyTorch
PyText is a deep-learning based NLP modeling framework built on PyTorch. PyText addresses the often-conflicting requirements of enabling rapid experimentation and of serving models at scale. It achieves this by providing simple and extensible interfaces and abstractions for model components, and by using PyTorch’s capabilities of exporting models for inference via the optimized Caffe2 execution engine. We use PyText at Facebook to iterate quickly on new modeling ideas and then seamlessly ship them at scale. Distributed-training support built on the new C10d backend in PyTorch 1.0. Mixed precision training support through APEX (trains faster with less GPU memory on NVIDIA Tensor Cores). Extensible components that allows easy creation of new models and tasks. -
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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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spaGO
Self-contained Machine Learning and Natural Language Processing lib
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 both for training and inference, easy to understand from start to finish. The core module of Spago relies only on testify for unit testing. In other words, it has "zero dependencies", and we are committed to keeping it that way as much as possible. Spago uses a multi-module workspace to ensure that additional dependencies are downloaded only when specific features (e.g. persistent embeddings) are used. A good place to start is by looking at the implementation of built-in neural models, such as the LSTM. Except for a few linear algebra operations written in assembly for optimal performance (a bit of copying from Gonum), it's straightforward Go code, so you don't have to worry. -
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NOTE: I couldn't keep up this project to align with latest Unicode spec. Not sure I may be continuing. You can try Myanmar3 from Myanmar NLP or WinUniInnwa or https://sourceforge.net/projects/prahita/ or something better compliant font. ~Victor --- [This is UniBurma - UniMM project workshop area. This project currently have two productions, UniBurma and UniMM. For more descriptive info about this project, please visit http://unimm.org/. You can browse lastest source from SVN trunk.]
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MARF is a general cross-platform framework with a collection of algorithms for audio (voice, speech, and sound) and natural language text analysis and recognition along with sample applications (identification, NLP, etc.) of its use, implemented in Java.
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Next Generation Programming
Compose Software Without Writing Any Programing Code
"Next Generation Programming - Programming Without Coding Software" is a drag-drop wizard for creating simple or complex applications without writing any programming language code The Software is coded/designed with "Java Programming Language" for novice/expert programmers; Programmers can write softwares with visual tools : drag-drop components;visual editors... Programmers can use the software to compose of simple/complex applications : Database programs, circuit design, generate code and upload to chip for designed circuits (ESP8266, ESP32 chips) The Software in question is much simpler to use than PWCT (https://sourceforge.net/projects/doublesvsoop/) software. The Software has more features than PWCT software such as SCADA. Please start by looking at examples from the website first. In this way, you can learn the features of the software and how to use the software in a very short time. More Information (Documents, Videos, Examples ...) : negep.epizy.com -
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Awesome Fraud Detection Research Papers
A curated list of data mining papers about fraud detection
A curated list of data mining papers about fraud detection from several conferences. -
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Awesome Recurrent Neural Networks
A curated list of resources dedicated to RNN
A curated list of resources dedicated to recurrent neural networks (closely related to deep learning). Provides a wide range of works and resources such as a Recurrent Neural Network Tutorial, a Sequence-to-Sequence Model Tutorial, Tutorials by nlintz, Notebook examples by aymericdamien, Scikit Flow (skflow) - Simplified Scikit-learn like Interface for TensorFlow, Keras (Tensorflow / Theano)-based modular deep learning library similar to Torch, char-rnn-tensorflow by sherjilozair, char-rnn in tensorflow, and much more. Codes, theory, applications, and datasets about natural language processing, robotics, computer vision, and much more. -
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This is a Java-based project for complex event extraction from text and co-reference resolution. Currently the code can read BioNLP shared task format (http://2011.bionlp-st.org/) and i2b2 Natural Language Processing for Clinical Data shared task format (https://www.i2b2.org/NLP/DataSets/Main.php). Event extraction includes finding events and the parameters for an event in a text. The method is based on SVM but other ML algorithms can be adopted. The method details are explained in the following paper: Ehsan Emadzadeh, Azadeh Nikfarjam, and Graciela Gonzalez. 2011. Double Layered Learning for Biological Event Extraction from Text. In Proceedings of the BioNLP 2011 Workshop Companion Volume for Shared Task, Portland, Oregon, June. Association for Computational Linguistic
