Python is widely regarded as the best programming language and is critical to artificial intelligence (AI) and machine learning tasks. Python is an extremely efficient programming language compared to other mainstream languages, and it’s an excellent choice for beginners thanks to its English-like commands and syntax. Another one of the best aspects of the Python programming language is that it consists of a huge amount of open source libraries, making it useful for a wide variety of tasks.
Python and NLP
Natural Language Processing, or NLP, is an area of AI that aims to understand the semantics and connotations of natural human languages. The interdisciplinary field combines techniques from linguistics and computer science, which are used to create technologies such as chatbots and digital assistants.
There are many aspects that make Python a great programming language for NLP projects, including its simple syntax and transparent semantics. Developers also have access to excellent support channels for integration with other languages and tools.
Perhaps the best aspect of Python for NLP is that it provides developers with a wide variety of NLP tools and libraries that allow them to perform a number of tasks such as topic modeling, document classification, part-of-speech (POS) tagging, word vectors, sentiment analysis, and Lake.
Let’s take a look at the 10 best Python libraries for natural language processing:
At the top of our list is Natural Language Toolkit (NLTK), which is widely regarded as the best Python library for NLP. NLTK is an essential library that supports tasks such as classification, tagging, stem, parsing and semantic reasoning. It is often chosen by beginners who want to get involved in NLP and machine learning.
NLTK is a very versatile library and it helps you create complex NLP functions. It provides you with a large number of algorithms to choose from for a particular problem. NLTK supports multiple languages, as well as named entities for multiple languages.
Since NLTK is a string processing library, it takes strings as input and returns strings or lists of strings as output.
Advantages and disadvantages of using NLTK for NLP:
- Most famous NLP library
- Third Party Extensions
- learning curve
- sometimes slow
- No neural network models
- Splits text by sentence only
SpaCy is an open-source NLP library designed explicitly for production use. SpaCy allows developers to create applications that can process and understand large amounts of text. The Python library is often used to build natural language understanding systems and information extraction systems.
One of the other great advantages of spaCy is that it supports tokenization for over 49 languages thanks to the fact that it is loaded with pre-trained statistical models and word vectors. Some of the key use cases for spaCy include autocomplete, autocorrect, analyzing online reviews, extracting key topics, and more.
Advantages and disadvantages of using spaCy for NLP:
- Easy to use
- Great for budding developers
- Relies on neural networks for training models
- Not as flexible as other libraries like NLTK
Another top Python library for NLP is Gensim. Originally developed for topic modeling, the library is now used for a variety of NLP tasks, such as document indexing. Gensim relies on algorithms to process input larger than RAM.
With its intuitive interfaces, Gensim realizes efficient multicore implementations of algorithms such as Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA). Some of the other top use cases of the library are finding text matching and converting words and documents to vectors.
Advantages and disadvantages of using Gensim for NLP:
- Intuitive interface
- Efficient implementation of popular algorithms such as LSA and LDA
- Designed for unattended text modeling
- Must be used often with other libraries like NLTK
Stanford CoreNLP is a library consisting of a variety of human language technology tools that aid in the application of linguistic analysis tools to a piece of text. With CoreNLP you can extract a wide variety of text properties such as recognition of named entities, tagging of part of speech and more with just a few lines of code.
One of the unique aspects of CoreNLP is that it includes Stanford NLP tools such as the parser, sentiment analysis, part-of-speech (POS) tagger, and named entity recognizer (NER). It supports a total of five languages: English, Arabic, Chinese, German, French and Spanish.
Advantages and disadvantages of using CoreNLP for NLP:
- Easy to use
- Combines different approaches
- Open source license
- Outdated interface
- Not as powerful as other libraries like spaCy
Pattern is a great option for anyone looking for an all-in-one Python library for NLP. It is a multi-purpose library that can handle NLP, data mining, network analytics, machine learning, and visualization. It contains modules for data mining from search engines, Wikipedia and social networks.
Pattern is considered one of the most useful libraries for NLP tasks, with features such as finding superlatives and comparatives, as well as finding facts and opinions. These features help it stand out from other top libraries.
Advantages and disadvantages of using Pattern for NLP:
- Data mining web services
- Network analysis and visualization
- Lack of optimization for some NLP tasks
A great option for developers who want to get started with NLP in Python, TextBlob provides good preparation for NLTK. It has a user-friendly interface that allows beginners to quickly learn basic NLP applications such as sentiment analysis and noun extraction.
Another top application for TextBlob is translations, which is impressive given its complex nature. That said, TextBlob inherits low performance from NLTK and should not be used for large scale production.
Advantages and disadvantages of using TextBlob for NLP:
- Great for beginners
- Provides foundation for NLTK
- User-friendly interface
- Low performance inherited from NLTK
- Not good for large scale production
PyNLPI, pronounced ‘pineapple’, is another Python library for NLP. It includes several custom Python modules for NLP tasks, and one of its top features is an extensive library for working with FoLiA XML (Format for Linguistic Annotation).
Each of the separate modules and packages is useful for basic and advanced NLP tasks. Some of these tasks include n-gram extraction, frequency lists, and building a simple or complex language model.
Advantages and disadvantages of using PyNLPI for NLP:
- Extraction of n-grams and other basic tasks
- Modular structure
Originally a third-party extension for the SciPy library, scikit-learn is now a standalone Python library on Github. It is used by big companies like Spotify, and there are many benefits to using it. First, it is very useful for classic machine learning algorithms, such as those for spam detection, image recognition, forecasting and customer segmentation.
That said, scikit-learn can also be used for NLP tasks such as text classification, which is one of the most important tasks in supervised machine learning. Another top case is sentiment analysis, which can help scikit-learn to analyze opinions or feelings through data.
Advantages and disadvantages of using PyNLPI for NLP:
- Versatile with a range of models and algorithms
- Built on SciPy and NumPy
- Proven track record of real-life applications
Near the end of our list is Polyglot, an open-source Python library used to perform various NLP operations. Based on Numpy, it’s an incredibly fast library that offers a wide variety of special commands.
One of the reasons Polyglot is so useful for NLP is that it supports extensive multilingual applications. The documentation shows that it supports tokenization for 165 languages, language detection for 196 languages, and part-of-speech tagging for 16 languages.
Advantages and disadvantages of using Polyglot for NLP:
- Multilingual with nearly 200 human languages in some tasks
- Built on top of NumPy
- Smaller community compared to other libraries like NLTK and spaCy
Our list of the 10 best Python libraries for NLP concludes with PyTorch, an open-source library created by Facebook’s AI research team in 2016. The library’s name is derived from Torch, an in-depth learning framework written in the Lua programming language.
PyTorch allows you to perform many tasks, and it is especially useful for deep learning applications such as NLP and computer vision†
Some of the best aspects of PyTorch are its fast execution speed, which it can achieve even when processing heavy graphs. It is also a flexible library that can work on simplified processors or CPUs and GPUs. PyTorch has powerful APIs that allow you to extend the library, as well as a natural language toolkit.
Advantages and disadvantages of using Pytorch for NLP:
- Robust framework
- Cloud platform and ecosystem
- General Machine Learning Toolkit
- Requires in-depth knowledge of key NLP algorithms