NLP (Natural Language Processing) trains computers to interpret texts and spoken words by replicating human coherence. NLP is one of the most prominent topics of Artificial Intelligence (AI) technology, which was only limited to NLP experts. Fortunately, the formerly developed NLP tools are now useful to aid in text preparation through traditional training strategies.
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Python is a significantly used language for Machine Learning, and its usage extends to cover NLP as well. To simplify text processing in ML, Python NLP libraries extend a helping hand in creating effective ML models and algorithms.Â
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Below are the best Python NLP libraries that can analyze human language and streamline text processing.
List of top Python NLP Libraries:
- spaCy
- NLTK
- PyNLPI
- Core NLP
- Gensim
- Polyglot
- Pattern
- AllenNLP
- Text Blob
- Scikit-Learn
1. spaCy:
spaCy is an agile and cost-effective Python library developed for sophisticated Natural Language Processing. The Python spaCy has been derived after cutting-edge research and was intended to be employed in real-world products.
It contains pre-trained pipelines. Currently, it supports tokenization and training for 60+ languages. This Python NLP library has advanced speed and neural network models for parsing, tagging, text classification, named entity identification, and other tasks.
The Python spaCy incorporates a production-ready training mechanism and simple model packaging, organization, and workflow management. spaCy uses neural networks for training and also has built-in word vectors.
The official documentation of spaCy is available here
2. NLTK (Natural Language Toolkit):
NLTK (Natural Language Toolkit) is a popular Python NLP library, extensively used for developing Python applications to communicate with human-language data.
NLTK helps with tasks like dividing sentences from paragraphs, identifying the portion of speech of particular phrases, accentuating its primary theme, and NLTK sentiment analysis, etc. This library is handy to ready text for future research, for instance, when using the models.
NLTK also helps translate words into numbers. It contains all the necessary tools for NLP.
List of prominent features of NLTK:
- The NLTK sentiment analysis
- Tokenization
- Text classification
- Parsing
- Part-of-speech tagging
- Stemming
You can install NLTK through the following command:
pip install NLTK
Check out the official documentation of NLTK here.
3. PyNLPI:
This Python library for NLP includes diverse modules for frequent and less frequent NLP tasks. It helps accomplish fundamental tasks like extracting n-grams and frequency lists and developing a simple language model.
It can read and process Moses++, GIZA, Taggerdata, SoNaR, and TiMBL data formats. An entire module is dedicated to working with FoLiA (the XML document format for annotating language resources like corpora).
Here you can get the official documentation of PyNLPI.
4. Core NLP:
CoreNLP helps develop linguistic annotations for text like parts of speech, token and sentence boundaries, named entities, sentiment, temporal and numeric values, dependency and constituency parser, quotation attributions, and relations between words.
This library supports human languages, including English, Chinese, Arabic, German, French, and Spanish. Although written in Java, it supports Python too. The working of Core NLP indicates accepting raw text, passing it across a series of NLP annotators, and producing a final set of annotations.
Get more information on CoreNLP with its official documentation.
5. Gensim:
Gensim is a renowned Python package for performing NLP jobs. Its exceptional feature leverages vector space modeling and topic modeling tools to analyze the semantic resemblance between two documents.
Containing algorithms are memory-independent for corpus size in this library. Therefore, it can deal with input larger than RAM. Two key features of Gensim are excellent memory use optimization and processing speed. Gensim works with massive datasets and can process data streams.
Gensim entails methods like the Latent Dirichlet Allocation (LDA), Random Projections (RP), Latent Semantic Analysis, Hierarchical Dirichlet Process (HDP), and word2vec deep learning. All these methods help solve the natural language issues.
Its other features include tf.idf vectorization, document2vec, word2vec, latent Dirichlet Allocation, and latent semantic analysis.
Gensim is widely used to spot text resemblances, transform documents and words to vectors, and summarise the text.
 You can install Gensim using: pip install gensim
Check out the official details of Gensim here.
6. Polyglot:
Polyglot is not as famous as other Python NLP libraries. However, it is still widely used to provide an extraordinary scope of analysis with the ability to encompass a variety of languages.
The high usage efficiency and simplicity turn it into an outstanding option for projects requiring a language SpaCy doesn’t support. Furthermore, the Polyglot package offers a CLI(command line interface)and library access via pipeline methods.
List of key features of Polyglot:
- Language Detection (supports 196 languages)
- Tokenization (supports 165 languages)
- Word Embeddings (supports 137 languages)
- Sentiment Analysis (supports 136 languages)
- Name Entity Recognition (supports 40 languages)
- Part of Speech Tagging (supports 16 languages)
Check out the complete documentation of Polyglot to get further details.
7. Pattern:
The Pattern Library is renowned for offering features like sentiment analysis, part-of-speech tagging, and vector space modeling. It supports a DOM parser, a web crawler, and Twitter and Facebook APIs. Its common use for web mining makes it inadequate to work on other natural language processing projects.
Typically, Pattern transforms HTML data into plain text and solves spelling errors in textual data. It has built-in tools for scraping various famous web services and sources, including Google, Facebook, Twitter, Wikipedia, Generic RSS, etc. All these tools are accessible as Python modules.
Pattern library uses few lower-level functionalities, allowing anyone to directly use NLP functions, vectors, n-gram search, and graphs.
Get to know more about the Pattern library from its official documentation.
8. AllenNLP:
When it comes to Natural Language Processing Tools, AllenNLP is one of the most cutting-edge libraries currently in the industry. It entails an assortment of libraries and tools that use PyTorch’s utilities.
Especially for research and business, it is a perfect choice. Instead of building a model from scratch with PyTorch, it is easier to make it with AllenNLP. Moreover, AllenNLP provides comprehensive NLP capabilities; however, it must be optimized for speed.
Key features of AllenNLP:
- Assists in text + vision multimodal tasks like Visual Question Answering (VQA)
- Classification tasks
- Pair classification
- Sequence tagging
To understand more about the usage and installation and usage of AllenNLP, check out its official documentation here.
9. TextBlob:
This Python NLP library is commonly used for NLP tasks like noun phrase extraction, voice tagging, classification, and sentiment analysis. It is based on the NLTK library. Frequently, it is used for sentiment analysis, spelling correction, and translation & language detection.
The user-friendly interface of TextBlob offers access to fundamental NLP tasks like word extraction, sentiment analysis, parsing, etc. For beginners, it is a perfect choice.
Key features of TextBlob:
- Helps in spelling correction
- Helps in noun phase extraction
- Supports a huge number of languages (range: 16 – 196) for various tasks
Learn more about the usage and installation of TextBlob with the official documentation available here.
10. Scikit-Learn:
Scikit-learn is one superlative library that offers a broad assortment of NLP algorithms and the latest features. These features and algorithms assist developers in creating machine learning models. Â
Scikit-learn has built-in class methods to manage text classification issues. Its outstanding documentation helps you reap the most of resources and its other famous packages for fundamental NLP operations.
It helps Python developers learn and build MLM. Moreover, it is a great choice for performing basic NLP operations. Various Automatic class methods are included.
You can get more details on the Scikit-Learn library from its official documentation.
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Conclusion:
Python NLP libraries help Python programmers to develop extraordinary text processing apps. These libraries can assist organizations in obtaining visual insights from data. Make sure to choose a Python NLP library by accessing the features and how they relate to each other as part of a single package.
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Which Python NLP library is suitable for complex data?
Scikit-learn is a well-known Python library that lets you deal with complex data. It is an open-source library that supports machine learning and is suitable for complex data.
Name the Python NLP library to work on multi-dimensional data.
Numpy (Numerical Python) is an extensively used Python NLP library that supports multi-dimensional data and large matrices. For easy calculations, it includes built-in mathematical functions.
Which is the biggest machine learning library?
PyTorch is the most extensive machine learning library that optimizes tensor computations. The rich APIs allow you to perform tensor calculations with powerful GPU acceleration.
Which Python NLP library is vastly used in the deep learning community?
Hugging Face Transformers is one of the most extensively used libraries in the NLP community. Because it provides native support for the Tensorflow and PyTorch-based models, it is now widely accepted in the deep learning community.
