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Although many languages are used for communication purposes, it is considered one of the most complex data formats. Have you ever wondered how voice assistants like Google Translate, Alexa, and Siri can understand, process, and respond to human commands? NLP is a branch of data science aimed at getting computers to understand the semantics and analyze textual data to extract meaningful insights. Some of the typical applications of natural language processing are:
- machine translation
- text summary
- voice recognition
- recommendation system
- sentiment analysis
- market intelligence
NLP Libraries are built-in packages for embedding NLP solutions into your applications. Libraries like this are very useful because they allow developers to focus on what really matters to the project. Below is an introduction to some of the most popular NLP libraries that you can use to build intelligent applications.
GitHub Star ⭐: Link to 11.8k GitHub repository: Natural Language Toolkit
NLTK is the best-known Python library for processing human language data. It offers an intuitive interface with over 50 corpora and vocabulary resources. It is a versatile open-source library that supports tasks such as classification, tokenization, POS tagging, word removal stopping, stemming, and semantic inference.
| Strong Points | Cons |
| comprehensive | steep learning curve |
| Massive community support | Can be slow and memory intensive |
| extensive documentation | |
| Customizable |
Useful resource
GitHub star ⭐: 25.7k Link to GitHub repository: SpaCy
SpaCy is an open source library developed for use in production environments. It is a great option for statistical NLP as it can process large amounts of text quickly. It comes with up to 80 pre-trained pipelines for 24 languages and currently supports tokenization for over 70 languages. Facilitates tasks such as POS tagging, dependency analysis, sentence boundary detection, named entity recognition, text classification, and rule-based matching, as well as providing various linguistic annotations to gain insight into the grammatical structure of the text provide. Such features greatly improve the accuracy and depth of NLP tasks.
| Strong Points | Cons |
| fast and efficient | Limited language support compared to NLTK |
| user friendly | |
| pre-trained model | The size of some pretrained models can be an issue for users with limited computing resources |
| Customizable model |
Useful resource
- SpaCy Online Documentation – Official Documentation
- SpaCy Online Course – Advanced NLP with SpaCy
- SpaCy Universe is a community-driven platform with tools, extensions and plugins built on top of SpaCy. Also includes demos and books to guide you – SpaCy Universe
GitHub star ⭐: 14.2k Link to GitHub repository: Gensim
Gensim is a popular Python library for topic modeling, document indexing, and similarity searching across large corpora. Provides a pre-trained model of word embeddings used to identify semantic similarities between two documents. For example, a pre-trained word2vec model can identify that “Paris” and “France” are related because Paris is the capital of France. The ability to identify such semantic relationships provides deep insight into the underlying meaning and context of your data. Gensim is very effective because it can handle inputs larger than available RAM.
| Strong Points | Cons |
| intuitive interface | Limited pretreatment capabilities |
| efficient and scalable | |
| Distributed computing support | Limited support for deep learning models |
| Offers a wide range of algorithms |
Useful resource
GitHub star ⭐: 8.9k Link to GitHub repository: Stanford CoreNLP
Stanford CoreNLP is one of the well-tested natural language processing tools written in Java. It takes raw human language as input and with just a few lines of code, it can perform various operations such as POS tagging, named entity recognition, dependency parsing, semantic analysis, and more. Originally designed for English, it now supports a large number of languages as well, but is not limited to Arabic, French, German, Chinese, etc. All in all, a robust and reliable open source tool for NLP tasks.
| Strong Points | Cons |
| high accuracy | outdated interface |
| extensive documentation | Scalability limits |
| Comprehensive linguistic analysis |
Useful resource
GitHub star ⭐: Link to 8.5k GitHub repository: TextBlob
TextBlob is another Python library used for processing text data. It comes with a very friendly and easy-to-use interface. It provides a simple API to perform tasks such as noun phrase extraction, part-of-speech tagging, sentiment analysis, tokenization, word and phrase frequency, parsing, and WordNet integration. Familiarize yourself with NLP tasks.
| Strong Points | Cons |
| beginner friendly | Poor performance |
| easy-to-use interface | Limited function |
| Integration with NLTK |
Useful resource
GitHub Star ⭐: 91.9k Link to GitHub repository: Hugface Transformers
Hugging Face Transformers is a powerful Python NLP library with thousands of pre-trained models that can be used to perform NLP tasks. These models have been trained on massive amounts of data and can understand underlying patterns in text data. Using pre-trained models saves developers time and resources compared to training their own models from scratch. Transformer models can also perform tasks such as table question answering, optical character recognition, information extraction from scanned documents, video classification, and visual question answering.
| Strong Points | Cons |
| user friendly | resource intensive |
| Large and active community | expensive cloud-based services |
| language support | |
| Reduced computing costs |
Useful resource
NLP libraries have played an important role in accelerating the progress of NLP research. This allowed machines to communicate effectively with humans. NLP tasks may seem a bit complicated at first, but with the right tools they can be handled very well. The list above refers only to the top libraries currently used in NLP, but there are many more that you can explore. I hope you have learned something of value from this article. We highly encourage you to try these tools and build great things.
Kanwal Maereen is an aspiring software developer with a keen interest in AI applications in data science and medicine. Kanwal has been named his Google Generation Scholar 2022 for the APAC region. Kanwal likes to share her tech knowledge by writing articles on trending topics and is passionate about improving the representation of women in the tech industry.
