Natural Language Processing (NLP) has become the inherent part of machine learning development in the last decade and python is the language that is being used most. In that case, developers are looking for NLP libraries python to perform NLP tasks.
Fortunately, we find NLP applications easily in the development landscape. But when it comes to application development with Python, it’s become a little bit confusing to find out the best Python NLP libraries.
This article will study the basics of NLP, its use, and alertness. While you recognize the NLP, it will let you know approximately the NLP libraries and their functions.
We can speak of the pinnacle top 11 Python NLP libraries right here. But before knowing about the most popular NLP libraries in Python, we have to determine what is natural language processing? Furthermore, what are the essential tasks of python text analytics library?
So, let’s roll over this article & take one more step forward to the deep learning models.
First thing first, Natural Language Processing is a ground of artificial intelligence. NLPs aim is to understand the semantic structure and meaning of the textual human languages.
It's far exceptional explained as AI for speech and textual content, which means there are computer and human languages for verbal exchange in computer science and AI. It allows machines to study natural human languages. As a middle branch of data science, NLP offers information to text and textual content to information.
When imposing the NLP method, we should understand the test data and other duties addressing automatic summarization, machine translation, and many others.
A few applications of NLP right here are-
Day by day, NLP is increasing its reach to us and the industry. As a result, there is a lot of textual data and greater scattered data, the ones we want to process.
There are a couple of NLP activities, including tokenizing, text mining, text modeling, word stemming, text classification, lemmatization, POS tagging, chunking, stop phrase elimination, named entity recognition, sentiment & text analysis, abstract semantic, machine translation, and dialogue systems, to name a few.
NLP libraries' purpose is to simplify text processing; a google NLP library is sufficient for the transformation of loose text into established text sentences those machines can utilize. Within the same manner, NLP libraries have a smooth get API where the brand new and big algorithms are successfully used.
Now it’s time to reveal the best Python NLP libraries for analyzing human language data text and their uses.
Natural Language Toolkit has widely recognized as one of the top text processing libraries. This NLTK Python package is introduced for smooth language processing.
Python NLTK is the most used Python NLP library, an open-source NLP library. POS tagging, phrase frequencies, NLTK sentiment analysis, etc. are the most powerful functions that it offers.
NLTK's user-friendly interfaces provide more than 50 linguistics assistants such as WordNet, corpora, linguistic, etc. Where those textual content processing libraries are used for class, tokenization, stemming, and so forth.
Uses of NLTK Package Python For Natural Language Processing
Sentiment analysis and take out the sentiment score.
Chatbot development.
| Pros | Cons |
|---|---|
| Foundation of NLP learning | Steep learning curve |
| Various extractive tools | Little bit slow for NLP production usage |
| NLTK sentiment analysis is highly rich | |
| All essential languages |
For more information, check out the official document of Natural Language Toolkit.
SpaCy, in simple words, it’s an advanced open-source tool for language processing in python.
This library comes up with spaCy projects like entity recognition, pre-trained statistical model, dependency parsing, text classification, word vector, tokenization deep learning integration, etc. It’s spaCy sentiment analysis this tool emphasizes art speed and accuracy, CNN models for tagging and translating.
Along with the compelling features & intuitive interface, spaCy boasts the “industrial-strength”.
| Pros | Cons |
|---|---|
| Works Faster | Support fewer languages than NLTK |
| Easy to learn & implement | Less flexibility |
| Neural Network models | |
| Designed for product usage |
Get more on the official spaCy documentation!
Genism is one of the most popular Python libraries for commercial, production-grade NLP solutions. This open-source Python library comes up with semantic reasoning machine learning models for similarity retrieval tasks.
For scientific computing, Genism highly depends on SciPy & NumPy. Wondering for thinking how to use Genism? Just relax; Genism has extensive documentation like a full-form user manual & Jupyter Notebook tutorials.
| Pros | Cons |
|---|---|
| Intuitive interface | Fewer customization options |
| Works pretty fast | Unsupervised text modeling |
| Good product development environment | |
| Integration flexibility with NLTK |
CoreNLP is a linguistics analysis tool widely also known as Stanford CoreNLP. Although CoreNLP is written in Java, it provides programming interfaces for almost all programming languages.
Furthermore, CoreNLP is the most-used package for entity recognition by a wide range of enterprises for their production implementation. Besides, this tool considers the best choice for sentiment analysis right after NLTK Python.
| Pros | Cons |
|---|---|
| Great for beginners | Little bit slow |
| Versatile | Not so good for production usage |
| Easy interface | |
| Excellent design prototypes |
Check out their official documentation for more information about CoreNLP.
TextBlob is another Python text processing library for initial prototyping. Based on the NLTK library, TextBlob was developed in Python 2 & 3. This popular Python library comes with familiar interfaces & strings of language processing tasks for almost all NLP pilot projects.
Despite missing neural networks, Python TextBlob has popular algorithms and a collection of functions for natural language processing. Their comprehensive documentation is pretty good for new practitioners of NLP with Python.
| Pros | Cons |
|---|---|
| Interactive interface | No word vector modules |
| Sentiment analyzer | Little bit slow |
| Easy to use & implement operations | |
| Google language translation & detection tool |
PyNLPl has separated modules and tools packages. Each of the modules works depending on the task's difficulty level. Wherein requires basic NLP actions; you can perform tasks including gram search, extracting n-grams, frequency lists, growing smooth language, & building language models.
On the other, this NLP library has advanced functions for NLP complex preprocessing tasks like data tagger, Moses ++, TiMBL data module, etc.
| Pros | Cons |
|---|---|
| Can work with FoLiA XML | Much slower |
| Amazing library | |
| Separated modules |
Click here for getting the PyNLPI documentation.
Polyglot is a language processing tool using Python. This underrated NLP library is on our top Python libraries list because of its extensive collection of advanced functions, analysis capabilities, development features, and an impressive collection of languages.
Polyglot allows larger multilingual applications similar to the spaCy. From my side, Polyglot is one of the best Python libraries for NLP in terms of its full features.
Features like speech recognition, POS tagger, sentiment analysis like NLTK sentiment analysis tools, tokenization etc.
| Pros | Cons |
|---|---|
| Language detection | Sharing & implementing different languages is difficult |
| Part of speech tagging | |
| Word embedding | |
| Transliteration |
Despite being lesser-known, they have comprehensive documentation, deciphering for the entry process too that’s what I mostly like about Polyglot.
The Pattern Library is an open-source Python library for performing some advanced tasks. It consists of mining tools for data mining or data science, searching, sentiment analysis, POS tagging, and network analysis tools for graphs & visualization.
When the names of the function are set as the way of self-explanatory patterns, learning can be valuable for the learners. Additionally, it could be a useful Python library for language processing framework for web developers.
Although this Python library is a perfect combination of brilliant features & resource compilation, all of those are lower-functional & still, it’s a web miner.
As a result, it allows you to do some NLP steps like n-gram, graphs, and NLP basic functions if you like. But Patten is still not good enough for other Python NLP operations.
| Pros | Cons |
|---|---|
| Network analysis | Fewer functions for complex tasks |
| Visualization | Not designed for all basic NLP tasks |
| Provides DOM parser | |
| Advanced data mining |
Scikit-learn is one of the best libraries for providing a wide range of NLP algorithms & advanced features that help developers build machine learning models.
Like libraries for classification, Scikit-learn has inherent class methods in order to handle text classification problems.
However, except for missing neural modeling features, Scikit-learn has excellent documentation that can help you take advantage of resources and its other popular packages for basic NLP operations.
| Pros | Cons |
|---|---|
| Automatic class methods | No Neural Networks model |
| Help developers learn & build MLM | Not designed for complex NLP operations. |
| Good for basic NLP operations |
Flair was developed through the Zalando research team. It's miles a really perfect and easy library in which it's miles designed over PyTorch.
We include Flair in this best natural language processing library list based on its increasing number of database query language support, easy access interface, POS tagging & name entity recognition.
Besides, Flair has some additional functionality, including code embedded, classification, etc. In terms of having pre-trained models, it’s easy to use & considered one of the cool Python libraries for NLP operations. Besides, the official resource of Flair is also available that can help you to know more about this Python library.
| Pros | Cons |
|---|---|
| Name recognizer | No sentiment analyzer |
| Custom models | Slow production usage |
| Text tagging | |
| Sense disambiguation & classification |
In this list of Python natural language processing libraries, Vocabulary is the last one. This is because it’s considered a great dictionary while practicing NLP operations with Python.
Vocabulary is used to do tasks like translation, pronunciation, synonyms, antonyms, meaning etc. This essential dictionary is written in simple Python with minimum dependencies.
In that case, read out its full documentation to get a clear idea about using vocabulary or not!
| Pros | Cons |
|---|---|
| Great dictionary | Not designed for complex NLP operations |
| Excellent substitute to WordNet | Only good for basic NLP activities |
| Easy install & use | |
| Can return all JSON objects |
Now you have detailed information about different but most useful Python libraries. We can see that most libraries have the same functionalities and accept some unique features of a couple of libraries to perform specific NLP operations & some common NLP tasks.
But the usage of these Python NLP libraries depends on the NLP problem to solve depending on the natural language processing projects.
Since Python has an active community here, developers build NLP tools to solve NLP problems but release them for public usage.
So, you could revel in studying Python programs and do loads extra, but we make your job easier until then!
