Natural language processing, or NLP, is best defined as “artificial intelligence for speech plus text.” Natural language processing, which powers voice commands, speech as well as text translation, sentiment analysis, text summarization, and a boatload of other technical applications & analyses, has indeed been greatly improved thanks to deep learning. The Python programming language offers a user-friendly interface for all types of machine learning, including NLP. In reality, the Python environment is brimming with natural language processing goodness. It’s indeed important to learn python for NLP. Throughout this article, we’ll look at several of the NLP libraries for Python.
Extracting relevant information from free text is required for developing chatbots, patent research and also analysis, voice/speech recognition, patient data processing, as well as searching photo content are among many other NLP use cases. NLP Libraries’ main purpose is to make textual preprocessing simpler.
Efficient NLP libraries ought to be able to convert free text phrases into structured attributes that can easily be fed onto Machine Learning or Deep Learning pipelines. Furthermore, an NLP library must have an intuitive API and the ability to quickly apply the most up-to-date algorithms and models.
We’ll discuss the top Python NLP Libraries in this article, even though there are numerous NLP Libraries created for specialized NLP applications.
Natural Language Toolkit (NLTK)
In Python, NLTK is a useful package that helps with categorization, stemming, labeling, parsing, semantics analysis, and tokenization. It is essentially your major machine learning as well as a natural language processing tool.
It now serves as a basis for Python developers who are getting their feet wet in the industry (and machine learning) why they learn to code machine learning softwares.. Steven Bird and Edward Loper of the University of Pennsylvania developed the library, which was essential in groundbreaking NLP research.
- NLTK, Python libraries, and other technologies are now used in many university courses throughout the world.
- This library is highly versatile, and it’s also quite challenging to utilize with Python for Natural Language Processing.
- NLTK is slow and therefore does not meet the needs of fast-paced production environments.
Although there is a massive learning curve, developers can use resources such as this useful book to understand more about the ideas behind the language processing jobs that this toolkit provides.
TextBlob
TextBlob is a must-have for Python developers out there who are getting started with NLP and would like to get the most out of their initial experience with NLTK.
It essentially gives newcomers an easy-to-use interface to assist them in learning its most basic NLP tasks, such as sentiment analysis, pos-tagging, and noun phrase extraction.
- This library, we honestly think, can be used by anyone and everyone who wishes to take their initial steps towards NLP using Python.
- It comes in handy while creating prototypes.
- Nevertheless, it also inherits NLTK’s major flaws: it’s way too slow to assist developers in dealing with the constraints of the NLP Python production application.
Gensim
Gensim is a Python library that uses vector space modelling as well as a topic modelling toolkit to find semantic similarities among two documents. With the support of an efficient data stream and incremental algorithms, it could handle big text collections, which is far greater than we could claim for competing packages that solely target bulk and in-memory processing.
- Its excellent memory use optimization plus processing speed are what we adore about it. These have been accomplished with the help of NumPy, a Python module.
- The vector space modeling capabilities of the program are also excellent.
CoreNLP
CoreNLP library was designed at Stanford and written in Java. It does, however, come with a wrapper for a variety of languages, namely Python. That is why it is handy for Python developers who want to fine-tune their skills at natural language processing.
What is CoreNLP’s most significant benefit?
- The library is extremely quick and well-suited for use in a product development environment.
- Furthermore, some CoreNLP elements could be combined with NLTK, increasing the latter’s performance.
spaCy
spaCy is a new library that was created with production in mind. It is because it is far more user-friendly than competing Python NLP packages such as NLTK.
- spaCy has the quickest syntactic parser just on market right now.
- Furthermore, because the toolkit is developed in Cython, it is extremely fast and reliable. Nevertheless, no tool is without flaws.
- In comparison to other libraries we’ve looked at so far, spaCy supports the fewest languages (seven).
However, given the expanding prominence of machine learning, natural language processing, and spaCy as a crucial library, the tool may soon handle more programming languages.
scikit–learn
This useful NLP package gives programmers access to a variety of algorithms for creating machine learning models. It has a lot of functionality for dealing with text categorization problems utilising the bag-of-words technique of building features.
- The innovative classes methods are the library’s asset.
- Additionally, scikit-learn comes with good documentation to assist programmers in making the most of its capabilities.
- The library, on the other hand, does not employ neural network models for text preprocessing.
If you really want to perform more complicated preprocessing activities for your text corpora, such as POS tagging, you should use alternative NLP packages first, then turn towards scikit-learn to develop your models.
polyglot
This little-known library is among our favorites because it provides a wide variety of analyses as well as extensive language coverage.
- It’s also extremely quick owing to NumPy.
- Polyglot is comparable to spaCy in that it is very effective, simple, and a viable solution for projects incorporating languages that spaCy does not support.
- The library stands out among the rest since it uses pipeline methods to request the use of a certain command inside the command line.
- It’s well worth your time to give it a shot.
Conclusion
Python is a leading technology for NLP. You can learn python online from websites like pythonforbeginners.com. In the realm of artificial intelligence, developing an application that can manage natural languages might be difficult. However, owing to this comprehensive toolbox and Python NLP modules, developers have everything they need to create remarkable tools. These eight libraries, as well as the language’s inherent properties, make it an excellent candidate for any application that depends on the machine translation of human languages.
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