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Different Languages are used for communication purposes but it is considered one of the most complex data forms to work with. Have you ever thought that how voice assistants like Google Translate, Alexa, and Siri are able to understand, process, and respond to human commands? It is possible because of Natural Processing Language. NLP is the branch of data science that aims at making computers understand the semantics and analyze the textual data to extract meaningful insights from it. Some of the typical applications of Natural Language Processing are as follows:
- Machine Translation
- Text Summarization
- Speech Recognition
- Recommendation Systems
- Sentiment Analysis
- Market Intelligence
NLP libraries are built-in packages to incorporate NLP solutions into your application. Such libraries are really useful as they enable developers to focus on what really matters for the project. Below is an introduction to some of the most popular NLP Libraries that can be used to build intelligent applications.
1. NLTK – Natural Language Toolkit
GitHub Stars ⭐: 11.8k Link to GitHub Repo: Natural Language Toolkit
NLTK is the most recognized Python library to process human language data. It provides an intuitive interface with over more than 50 corpora and lexical resources. It is a versatile and open-source library that supports tasks like classification, tokenization, POS tagging, stopping word removal, stemming, semantic reasoning, etc.
| Pros | Cons |
| Comprehensive | Steep Learning Curve |
| Large Community Support | Can be slow & Memory Intensive |
| Extensive Documentation | |
| Customizable |
Useful Resources
- NLTK Documentation – Official Website
- Natural Language Processing with Python and NLTK – Udemy Course
- Analyzing Text with Natural Language Toolkit Book – NLTK Book
2. SpaCy
GitHub Stars ⭐: 25.7k Link to GitHub Repo: SpaCy
SpaCy is an open-source library developed to be used in production environments. It can quickly process high volumes of text making it a perfect option for statistical NLP. It comes with up to 80 pre-trained pipelines for 24 languages and currently supports tokenization for 70+ languages. Besides facilitating tasks like POS tagging, Dependency Parsing, Sentence Boundary Detection, Named Entity Recognition, Text Classification, Rule-based Matching, etc it also provides a variety of linguistic annotations to give you insights into a text’s grammatical structure. Such features greatly enhance the accuracy and depth of the NLP Tasks.
| Pros | Cons |
| Fast & Efficient | Supports limited languages as compared to NLTK |
| User-Friendly | |
| Pre-trained models | The size of some pre-trained models may be of concern to users with limited computing resources |
| Allows Model Customization |
Useful Resources
- SpaCy Online Documentation – Official Docs
- SpaCy Online Courses – Advanced NLP with SpaCy
- SpaCy Universe is a community-driven platform with tools, extensions, and plugins built on top of SpaCy. It also contains demos and books for guidance – SpaCy Universe
3. Gensim
GitHub Stars ⭐: 14.2k Link to GitHub Repo: Gensim
Gensim is a Python library popularly known for topic modeling, document indexing, and similarity retrieval with large corpora. It offers pre-trained models for word embeddings that are used to identify the semantic similarity between the two documents. For instance, a pre-trained word2vec model can identify that “Paris” and “France” are related as Paris is the capital of France. The ability to identify such semantic relationships provides deep insights into the underlying meaning and context of data. The ability to process large inputs than the RAM available makes Gensim extremely effective.
| Pros | Cons |
| Intuitive Interface | Limited PreProcessing Capabilities |
| Efficient and Scalable | |
| Support for Distributed Computing | Limited support for Deep Learning Models |
| Offers a wide range of Algorithms |
Useful Resources
- Gensim Documentation – Official Docs
- Tutorial by TutorialPoint – Gensim Tutorial
4. Stanford CoreNLP
GitHub Stars ⭐: 8.9k Link to GitHub Repo: Stanford CoreNLP
Stanford CoreNLP is one of the well-tested Natural Language Processing tools written in Java. It takes the raw human language as the input and can perform a wide variety of operations like POS tagging, Named Entity Recognition, dependency parsing, and semantic analysis with just a few lines of code. Although it was originally designed for English, now it also supports numerous languages but is not limited to Arabic, French, German, Chinese, etc. Overall, it's a robust and reliable open-source tool for NLP tasks.
| Pros | Cons |
| High Accuracy | Outdated Interface |
| Extensive Documentation | Limited Scalability |
| Comprehensive Linguistic Analysis |
Useful Resources
- Stanford CoreNLP Homepage – Documentation & Explanation
- Overview with examples – GitHub Link
5. TextBlob
GitHub Stars ⭐: 8.5k Link to GitHub Repo: TextBlob
TextBlob is another Python library used for processing textual data. It comes with an extremely friendly and easy-to-use interface. It provides a simple API to perform tasks like Noun phrase extraction, Part-of-speech tagging, Sentiment analysis, Tokenization, Word and phrase frequencies, Parsing, WordNet integration, etc. I would personally recommend this to entry-level programmers who want to acquaint themselves with NLP tasks.
| Pros | Cons |
| Beginner Friendly | Slower Performance |
| Easy-to-use Interface | Limited Features |
| Integration with NLTK |
Useful Resources
- Official TextBlob Documentation: TextBlob
- Analytics Vidhya TextBlob Tutorial: Making NLP Easy with TextBlob
- Natural Language Basics with TextBlob – Short NLP Course
6. Hugging Face Transformers
GitHub Stars ⭐: 91.9k Link to GitHub Repo: Hugging Face 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 are trained on vast amounts of data and can understand the underlying patterns in the textual data. Using pre-trained models saves the time and resources of the developer as compared to training their own models from scratch. Transformer models can also perform tasks like table question answering, optical character recognition, information extraction from scanned documents, video classification, and visual question answering.
| Pros | Cons |
| Easy to Use | Resource Intensive |
| Large and Active Community | Expensive cloud-based services |
| Language Support | |
| Lower compute costs |
Useful Resources
- Official Documentation – Hugging Face Transformer Documentation
- Hugging Face Community Forum – Community Forum
- Advanced Introduction to Hugging Face Transformers – Coursera
Conclusion
NLP libraries have played a significant role in accelerating the progress in NLP research. It has enabled machines to communicate effectively with humans. Although NLP tasks may seem a bit complicated at first with the right tools you can handle them really well. The above-mentioned list only refers to only the top libraries currently being used in NLP but there is much more out there that you can explore. I hope you learned something valuable from this article and I would really encourage you to try out these tools and build something cool.
Kanwal Mehreen is an aspiring software developer with a keen interest in data science and applications of AI in medicine. Kanwal was selected as the Google Generation Scholar 2022 for the APAC region. Kanwal loves to share technical knowledge by writing articles on trending topics, and is passionate about improving the representation of women in tech industry.
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