Natural language processing (NLP) is a field located at the intersection of data science and Artificial Intelligence (AI) that – when boiled down to the basics – is all about teaching machines how to understand human languages and extract meaning from text. This is also why machine learning is often part of NLP projects.
But why are so many organizations interested in NLP these days? Primarily, these technologies can provide them with a broad range of valuable insights and solutions that address language-related problems consumers might experience when interacting with a product.
There’s a reason why tech giants like Google, Amazon, or Facebook are pouring millions of dollars into this line of research to power their chatbots, virtual assistants, recommendation engines, and other solutions powered by machine learning.
Since NLP relies on advanced computational skills, developers need the best available tools that help to make the most of NLP approaches and algorithms for creating services that can handle natural languages.
What is an NLP library?
In the past, only experts could be part of natural language processing projects that required superior mathematics, machine learning, and linguistics knowledge. Now, developers can use ready-made tools that simplify text preprocessing to concentrate on building machine learning models.
There are many tools and libraries created to solve NLP problems. Read on to learn more about nine excellent Python Natural Language Processing libraries that have, over the years, helped us deliver quality projects to our clients.
Why use Python for Natural Language Processing (NLP)?
There are many things about Python that make it a perfect programming language choice for an NLP project. This language's simple syntax and transparent semantics make it an excellent choice for projects that include Natural Language Processing tasks.
Moreover, developers can enjoy great support for integrating other languages and tools that come in handy for techniques like machine learning.
But something else about this versatile language makes it an ideal technology for helping machines process natural languages. It provides developers with an extensive collection of NLP tools and libraries that enable them to handle many NLP-related tasks, such as document classification, topic modeling, part-of-speech (POS) tagging, word vectors, and sentiment analysis.
List of NLP tools and libraries
NLTK is an essential library that supports tasks such as classification, stemming, tagging, parsing, semantic reasoning, and tokenization in Python. It's your primary tool for natural language processing and machine learning. Today it serves as an educational foundation for Python developers who are dipping their toes in this field (and machine learning).
The library was developed by Steven Bird and Edward Loper at the University of Pennsylvania and played a crucial role in breakthrough NLP research. Many universities around the globe now use NLTK, Python libraries, and other tools in their courses.
This library is pretty versatile, but we must admit that it’s also quite challenging to use for Natural Language Processing with Python. NLTK can be relatively slow and doesn’t match the demands of quick-paced production usage. The learning curve is steep, but developers can take advantage of resources like this helpful book to learn more about the concepts behind the language processing tasks this toolkit supports.
TextBlob is a must for developers who are starting their journey with NLP in Python and want to make the most of their first encounter with NLTK. It provides beginners with an easy interface to help them learn the most basic NLP tasks like sentiment analysis, pos-tagging, or noun phrase extraction.
We believe anyone who wants to make their first steps toward NLP with Python should use this library. It’s very helpful in designing prototypes. However, it also inherited the main flaws of NLTK – it’s just too slow to help developers who face the demands of NLP Python production usage.
This library was developed at Stanford University and written in Java. Still, it’s equipped with wrappers for many languages, including Python. That's why it can be helpful for developers interested in trying their hand at natural language processing in Python.
What is the most significant advantage of CoreNLP? The library is high-speed and works well in product development environments.
Moreover, some CoreNLP components can be integrated with NLTK, which is bound to boost the latter's efficiency.
Gensim is a Python library that identifies semantic similarity between two documents through vector space modeling and topic modeling toolkits. It can handle large text corpora with the help of efficient data streaming and incremental algorithms, which is more than we can say about other packages that only target batch and in-memory processing.
What we love about it are its small memory footprint, usage optimization and processing speed. These were achieved with the help of another Python library, NumPy. The tool's vector space modeling capabilities are also top-notch.
spaCy is a relatively young library designed for production usage. That’s why it’s much more accessible than other Python NLP libraries like NLTK. spaCy offers the fastest syntactic parser available on the market today.
Moreover, since the toolkit is written in Cython, it’s also really speedy and efficient.
However, no tool is perfect.
Compared to the libraries we have covered so far, spaCy supports the smallest number of languages (seven). However, the growing popularity of machine learning, NLP, and spaCy, as key libraries, means that the tool might start supporting more programming languages soon.
This slightly lesser-known library is one of our favorites because it offers a broad range of analysis and impressive language coverage. Thanks to NumPy, it also works fast. Using polyglot is similar to spaCy – it’s very efficient, straightforward, and an excellent choice for projects involving a language spaCy doesn’t support. The library also stands out from the crowd because it requests a dedicated command in the command line through the pipeline mechanisms—worth a try.
This handy NLP library provides developers with a wide range of algorithms for building machine-learning models. It offers many functions for the bag-of-words method of creating features to tackle text classification problems. The strength of this library is the intuitive class methods. Also, scikit-learn has excellent documentation that helps developers make the most of its features.
However, the library doesn't use neural networks for text preprocessing. So if you'd like to carry out more complex preprocessing tasks like POS tagging for your text corpora, it's better to use other NLP libraries and then return to scikit-learn for building your models.
Another gem in the NLP libraries Python developers use to handle natural languages. The Pattern allows part-of-speech tagging, sentiment analysis, vector space modeling, SVM, clustering, n-gram search, and WordNet. You can take advantage of a DOM parser, a web crawler, and helpful APIs like Twitter or Facebook. Still, the tool is a web miner and might not be enough to complete other natural language processing tasks.
HuggingFace has been gaining prominence in Natural Language Processing (NLP) since transformers' inception. It is an AI community and Machine Learning platform created in 2016 by Julien Chaumond, Clément Delangue, and Thomas Wolf.
Its goal is to provide Data Scientists, AI practitioners, and Engineers with immediate access to over 20,000 pre-trained models based on the state-of-the-art pre-trained models available from the Hugging Face hub.
These models can be applied to:
- Text in over 100 languages for classification, information extraction, question answering, generation, generation, and translation.
- Speech, for tasks such as object audio classification and speech recognition.
- Vision for object detection, image classification, and segmentation.
- Tabular data for regression and classification problems.
- Reinforcement Learning transformers.
Hugging Face Transformers also provides almost 2000 data sets and layered APIs. Thanks to nearly 31 libraries, programmers can efficiently work with those models. Most are deep learning, such as PyTorch, TensorFlow, JAX, ONNX, Fastai, Stable-Baseline 3, etc.
Take advantage of Python for NLP
When it comes to natural language processing, Python is a leading technology. Developing software that can handle natural languages in the context of artificial intelligence can be challenging. But thanks to this extensive toolkit and Python NLP libraries, developers get all the support they need while building unique tools.
These eight libraries and the innate characteristics of this fantastic programming language make it a top choice for any project that relies on machine understanding of human languages.
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