Open-source software (OSS) commands that the source code of an open-source project is openly available and may be redistributed and revised by an alliance of developers. Open-source projects enfold active community, collaboration, and transparency values for the given advantages of the platform and its users. There is no ideal moment to open source your project. You can open source an idea, a task in progress, or after years of being closed source. Generally addressing, you should open source your work when you feel satisfied with others’ aspects and feedback on your work.
Natural language processing (NLP) is a part of the scientific study of language (linguistics), computer science, and artificial intelligence involved with the interplays linking machines and human language, especially how to program computers to prepare and interpret massive amounts of natural language data.
In natural language processing, human language is split into fragments. The grammatical arrangement of sentences and the significance of words can be examined and explained in context. It helps machines to learn, understand and comprehend spoken or written text in the identical approach as humans.
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It’s a technology that multiple people use every day and has been around for ages but is frequently taken for granted. A quick example of NLP that people practice every day is spell check or talking to your google home or Alexa. NLP enables the machines to read text, learn speech, understand it, measure sentiment from the speech and determine which sections of the sentence are relevant to us and which section is not.
Now it’s time to move on to see top open-source NLP projects on GitHub. All the five open-source NLP projects examples mentioned in today’s article are fully open-source, easily available on GitHub, and all set for you to clone, modify, and extend them.
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So let get into it.
1. Gensim – 12.3K Stars & 4k Forks
Official Documentation: https://radimrehurek.com/gensim/
Gensim is an open-source library build on top of Python and frequently employed for general natural language tasks such as document indexing, topic modelling, and similarity retrieval. Gensim aims to deliver the functionality of its end audience, which is the natural language processing and information retrieval community.
Gensim is supported on Ubuntu (Linux), Windows and macOS X, and other additional platforms supporting Python and its libraries like NumPy. Gensim can treat arbitrarily large corpora employing data-streamed algorithms. Therefore, it is deemed to be one of the most sophisticated Machine Learning libraries.
All Gensim source code is maintained on GitHub beneath the GNU LGPL license. Its open-source society confirms this licence. The Gensim society also publishes pretrained models for fields like legal or health via the Gensim-data project.
2. Rasa – 11.7K Stars & 3.6k Forks
Official Documentation: https://rasa.com/docs/
Rasa is an open-source ML framework to automate text-based and voice-based discussions. With Rasa, you can develop contextual assistants above:
- Facebook Messenger
- Webex Teams
- Microsoft Bot Framework
- Google Hangouts
- Your custom conversational channels
and voice assistants like:
- Google Home Actions
- Alexa Skills
Rasa assists you in developing contextual assistants competent in producing layered conversations with loads of back-and-forth. For a person to have a significant replacement with a contextual assistant, the assistant demands to apply context to create things previously presented to it – Rasa allows you to develop assistants to achieve this in a scalable design.
3. Flair – 10.6K Stars & 1.7k Forks
Official Documentation: https://pypi.org/project/flair/0.8.0.post1/
Flair is a robust NLP library built on top of Python that empowers you to implement state-of-the-art NLP models for your documents, such as named entity recognition (NER), part-of-speech tagging (PoS), special provision for biomedical data, sense disambiguation and classification, with the support for rapidly expanding languages.
Flair has a very interactive and simple access interface that empowers you to manage and compare distinct word and record embeddings, including proposed Flair embeddings, ELMo embeddings, and BERT embeddings.
Flair has an interactive framework for state-of-the-art NLP. Its framework is built directly on PyTorch. It presents it as simple to train your models and experiment with distinct methods utilizing Flair embeddings and classes.
4. TextBlob: Simplified Text Processing – 7.7K Stars & 1k Forks
Official Documentation: https://textblob.readthedocs.io/en/dev/
TextBlob is a library for processing textual data compatible with Python2 and Python3. It implements a simple API for treating standard natural language processing (NLP) tasks. Tasks that TextBlob can achieve include part-of-speech tagging, classification, translation, noun phrase extraction, sentiment analysis, and more.
Fascinating highlights of TextBlob are:
- Noun phrase extraction
- Part-of-speech tagging
- Sentiment analysis
- Classification (Naive Bayes, Decision Tree)
- Tokenization (splitting text into words and sentences)
- Word and phrase frequencies
- Word inflection (pluralization and singularization) and lemmatization
- Spelling correction
- Add new models or languages through extensions
- WordNet integration
5. Stanza – 5.6K Stars & 721 Forks
Official Documentation: https://stanfordnlp.github.io/stanza/
Stanza is a natural language analysis package built on top of Python. It includes tools, which can be practised in a pipeline, to transform a string including human language text into lists of sentences and words, to create base forms of these words, their elements of speech and morphological characteristics, to provide a syntactic structure dependency parse, and to identify named entities.
The toolkit is intended to be lateral among more than 70 languages. Stanza is developed with extremely precise neural network segments that promote effective training and evaluation with individual annotated datasets.
Stanza is formed on top of the PyTorch library. You will get much more agile performance if you manage the software on a GPU-enabled computer.
Bonus: As a bonus component, here is a link to a great repository that includes resources related to Natural Language Processing.
Awesome Nlp – 12.2K Stars & 2.2 Forks
Awesome NLP is an open-source repository that includes a curated list of resources devoted to Natural Language Processing (NLP). The repository comprises of:
- Reading Content on general machine learning
- Introductions and Guides to NLP
- Blogs and Newsletters
- Videos and Online Courses
- Books & Tutorials
- Annotation Tools
- Text Embeddings Techniques
- Multilingual NLP Frameworks
With that, we have arrived at the end of our report. Here are the top five NLP projects on GitHub that are wonderful for sharpening your coding and project development skills.
These were the few of the widely adopted open-source NLP projects out there now. How many of the above-discussed projects have you heard about? If not, you can try them out and if you have any suggestions for me to introduce in the preceding list? Let me know.
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