Stanza vs Wit
Overview
Share your Stack
Help developers discover the tools you use. Get visibility for your team's tech choices and contribute to the community's knowledge.
CLI (Node.js)
Manual
Detailed Comparison
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. | It 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. |
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 | Native Python implementation requiring minimal efforts to set up;
Full neural network pipeline for robust text analytics, including tokenization, multi-word token (MWT) expansion, lemmatization, part-of-speech (POS) and morphological features tagging, dependency parsing, and named entity recognition;
Pretrained neural models supporting 66 (human) languages;
A stable, officially maintained Python interface to CoreNLP |
Statistics | |
GitHub Stars - | GitHub Stars 7.6K |
GitHub Forks - | GitHub Forks 926 |
Stacks 12 | Stacks 9 |
Followers 57 | Followers 34 |
Votes 0 | Votes 0 |
Integrations | |
| No integrations available | |
What are some alternatives to Wit, Stanza?
rasa NLU
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.
SpaCy
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.
Speechly
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.
MonkeyLearn
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.
Jina
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.
Sentence Transformers
It provides an easy method to compute dense vector representations for sentences, paragraphs, and images. The models are based on transformer networks like BERT / RoBERTa / XLM-RoBERTa etc. and achieve state-of-the-art performance in various tasks.
FastText
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.
CoreNLP
It provides a set of natural language analysis tools written in Java. It can take raw human language text input and give the base forms of words, their parts of speech, whether they are names of companies, people, etc., normalize and interpret dates, times, and numeric quantities, mark up the structure of sentences in terms of phrases or word dependencies, and indicate which noun phrases refer to the same entities.
Flair
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.
Transformers
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.
