Wit enables developers to add a modern natural language interface to their app or device with minimal effort. Precisely, Wit turns sentences into structured information that the app can use. Developers don’t need to worry about Natural Language Processing algorithms, configuration data, performance and tuning. Wit encapsulates all this and lets you focus on the core features of your apps and devices. | You can use it to extract information about people, places, events and much more, mentioned in text documents, news articles or blog posts. You can use it to understand sentiment about your product on social media or parse intent from customer conversations happening in a call center or a messaging app. You can analyze text uploaded in your request or integrate with your document storage on Google Cloud Storage. |
Voice-enabled Android and iOS apps;Rasberry Pi based home automation commanded by speech;Google Glass apps accepting voice commands;Robots and drones dialog interfaces (ROS);SMS-based information or remote control services;IM-based information or remote control services;"Quick add" features a la Google Calendar (replacing a form with free text input);Natural Language querying a la Facebook Graph Search (turning a sentence into a database query);Personal Assistants a la Apple’s Siri | - |
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rasa NLU (Natural Language Understanding) is a tool for intent classification and entity extraction. You can think of rasa NLU as a set of high level APIs for building your own language parser using existing NLP and ML libraries.

It 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. It comes with pre-trained statistical models and word vectors, and currently supports tokenization for 49+ languages.

It can be used to complement any regular touch user interface with a real time voice user interface. It offers real time feedback for faster and more intuitive experience that enables end user to recover from possible errors quickly and with no interruptions.

Turn emails, tweets, surveys or any text into actionable data. Automate business workflows and saveExtract and classify information from text. Integrate with your App within minutes. Get started for free.

It is geared towards building search systems for any kind of data, including text, images, audio, video and many more. With the modular design & multi-layer abstraction, you can leverage the efficient patterns to build the system by parts, or chaining them into a Flow for an end-to-end experience.

It is an open-source, free, lightweight library that allows users to learn text representations and text classifiers. It works on standard, generic hardware. Models can later be reduced in size to even fit on mobile devices.

Flair allows you to apply our state-of-the-art natural language processing (NLP) models to your text, such as named entity recognition (NER), part-of-speech tagging (PoS), sense disambiguation and classification.

It provides general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet…) for Natural Language Understanding (NLU) and Natural Language Generation (NLG) with over 32+ pretrained models in 100+ languages and deep interoperability between TensorFlow 2.0 and PyTorch.

It is a Python library for topic modelling, document indexing and similarity retrieval with large corpora. Target audience is the natural language processing (NLP) and information retrieval (IR) community.

Amazon Comprehend is a natural language processing (NLP) service that uses machine learning to discover insights from text. Amazon Comprehend provides Keyphrase Extraction, Sentiment Analysis, Entity Recognition, Topic Modeling, and Language Detection APIs so you can easily integrate natural language processing into your applications.