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8 Most Trending Python Libraries for Natural Language Processing (NLP) [2024 Update]

By Nivin Biswas Category Python Reading time 9-10 mins Published on Feb 02, 2023 Updated On Dec 14, 2024

Know The Best Python Libraries for Natural Language Processing With Python

Nowadays, intelligent machines, gadgets, and applications are everywhere, whether it’s a tool for effective marketing, business or a small smart wearable to track your health. Also, with the blessings of voice control technologies like ‘Siri' and 'Alexa,’ we are now well introduced to smart lights, smart fans, smart ACs, etc. And all these techs are powered by Natural Language Processing (NLP).

With the growing demand for smart devices and robotic appliances, the demand for advanced NLP skills is also approaching its peak. Python libraries for NLP lift the quality of text processing and make it accessible to its users. Thus, knowledge of Python libraries has become crucial.

An illustration titled 'Natural language processing' shows a person standing in front of a display holding a tablet. A chat bubble from him reads, 'Hi!'. That connects to a laptop display, from which a call out shows a python logo. A second chat bubble from the same display reads, 'Hello!'

In the present scenario, out of 10 guys with a computer science background, at least six are pursuing an advanced AI & ML Certification Program. Such programs usually come with the mandatory option of doing a capstone project. And being the most in-demand subfield, approximately 70% of learners choose a project related to natural language processing with Python (as Python is the easiest-to-adapt option for beginners).

If you also belong to such an enthusiastic NLP community, this blog will be a fruitful investment of your time.

Numerous NLP libraries are there, but you must know the present trends.

To be more specific, we can easily state that AI and NLP are subsets that focus on creating intelligent machines. We can't distinguish between the two based on their characteristics, but AI has the capacity to concentrate more on machine learning and data learning. On the other hand, natural language processing is concerned with teaching machines to understand human language and act intelligently in response to it.

8 Mostly Used Python NLP Librariesmachine learning

Here is the list of Python libraries commonly used with NLP levelling up project qualities.

1. Natural Language Toolkit:-

The Natural Language Toolkit (NLTK) is a powerful natural language processing library that has grown in popularity in recent years. It is ideal for developing ML model projects like:-

  • Sentimental evaluation

  • Tagging a portion of the speech

  • Creation of a thesaurus

NLTK is used in many great projects on GitHub, and this is one of the simple-to-use natural language processing libraries in Python. In addition to that, we can use NLTK in every OS we want.

An illustration titled 'Natural Language Toolkit' shows a man doing a mechanical task with a robot machine.

Advantages of Natural Language Toolkit:

  • We can easily use all sorts of third-party extensions.

  • Widely used NLP techniques/libraries

Disadvantages of Natural Language Toolkit:

When the demand for production usage is high, it can become laggy and slow. Furthermore, for some users, the skill set can be expensive.

Despite these drawbacks, NLTK provides several features; it is used in a variety of applications, including machine translation, information extraction, information retrieval, computational linguistics, text mining, summarization, text analysis, and many others.

2. spaCy:-

spaCy, which stands for Speech to Parse and Compile, is an open-source Python library for NLP. It is primarily intended for production use creating real-world projects and it aids in the handling of large amounts of text data. Because this toolkit is written in Python and Cython, it is much faster and more efficient when dealing with large amounts of text data. Hence, in the case of a project that needs compelling text stemming from NLP, spaCy can be a wise option.

Advantages of spaCy:-

  • It offers multi-trained rectifiers such as BERT.

  • It is significantly faster than other Python NLP libraries.

  • In more than 49 languages, it provides linguistically motivated tokenization.

  • It consists of 17 different languages and 55 trained pipelines.

Disadvantages of spaCy:-

  • The overall internal is not configurable.

  • A minimal amount of NLP abstraction and insufficient processing speed

3. Gensim:-

Gensim is a Python-recognized library that can be used for NLP tasks. This library primarily makes use of the unique features of vector modeling and top modeling toolkits to maintain similar interpretations between data. It was developed in 2008 by Tomas Mikolov and a team of researchers. The Gensim algorithm is built on a neural network based on a text generator.

However, it is now used for a wide range of natural language processing with Python, including document indexing. To process input larger than RAM, Gensim relies on algorithms.

In Gensim, Latent Dirichlet Allocation (LDA) and Latent Semantic Analysis (LSA) have efficient multiple core deployments. Other popular use cases for this Python NLP library include finding text similarities and converting words and documents to vectors.

An image titled, 'gensim'.

Advantages of Gensim:-

  • It features a simple UI experience.

  • Highly scalable performance within a system processing demand.

  • Popular algorithms such as LSA and LDA are implemented efficiently and with ease.

Disadvantages of Gensim:-

  • The Gensim is only for unsupervised machine learning models.

  • We cannot imply a full NLP pipeline, so the use of other libraries, such as NLTK and spaCy, is critical.

4. CoreNLP:-

Stanford CoreNLP is a library that contains a number of human language technology tools that aid in the application of linguistic analysis tools to text. CoreNLP can extract various text properties, including named-entity recognition and part-of-speech tagging.

CoreNLP is distinguished by the incorporation of Stanford NLP techniques and tools, such as the

  • Parser

  • Sentiment analysis

  • Part-of-speech (POS) tagger

  • Named entity recognizer (NER)

Advantages of CoreNLP:-

  • The tool is flexible and user-friendly.

  • It alternately combines a number of strategies.

  • Open-source tools can be easily available.

Disadvantages of CoreNLP:-

  • It has memory leakage usage.

  • It is not free to use; you must pay a fee for long-term use.

5. Scikit-learn:-

Scikit-learn is a Python library for natural language processing that offers a large number of algorithms for developing NLP and deep learning models. It has significant insights that help data scientists simplify learning. The main advantage is that it supports multiple programming languages, allowing data scientists to use it whenever possible.

It has many functions for bag-of-words to convert text into numerical vectors, but it also has some drawbacks. Additionally, it does not use neural networks for text processing, making it the best NLP tool for processing complex data.

Advantages of Scikit-learn:-

  • It has a diverse set of models and algorithms.

  • It has been Powered by SciPy and NumPy.

  • We can easily track down real-world applications.

Disadvantages of Scikit-learn:-

  • It is unable to function with AutoML.

  • It cannot perform deep learning pipelines.

6. Polyglot:

Polyglot is not only used for NLP; it is also used by developers in a variety of industries and for other software. Polyglot is a Python NLP library that is useful for multilingual applications. In addition, it has 196 languages for language detection and 26 languages for part speech tagging.

An image titled, 'POLYGLOT' shows a cloud structure that connects multiple human languages.

Advantages of using polyglot:-

  • Multilingual, with close to 200 human languages used in some tasks.

  • Comprehensively rely on NumPy.

Disadvantages of polyglots:-

  • The management of databases requires resources.

  • Because data layouts can be complex and debugging takes time, testing can be challenging.

7. TextBlob:-

TextBlob is a Python-based open-source natural language processing library (Python 2 and Python 3). TextBlob is the fastest NLP library for Python among all libraries. It is appropriate for beginners. It is a must-have resource for data scientists just starting with Python.

TextBlob appears to be an outstanding tool for people who are enthusiastic about and interested in using NLP; it has an easy-to-use interface that can perform sentiment analysis, phrase extraction, and other tasks. The following are some of TextBlob's key features:-

  • Sentimental analysis

  • Parsing

  • Frequency of words and phrases

  • Tagging parts of speech

  • The N-gram

  • Spelling improvement

  • Tokenization

  • Classification (Naive Bayes decision tree)

  • Extraction of noun phrases

  • Integration with WordNet

Advantages of TextBlob:-

  • Excellent tools for beginners.

  • Provides the foundation for NLTK (Natural language toolkit).

  • User-friendly interface for users.

Disadvantages of TextBlob:-

  • It lacks a neural network model.

  • There are no global integrated vectors.

8. PyNLPI:-

Data from various sources is collected, stored, and used in both the public and private sectors in the digital age. It is critical for digital libraries to handle large amounts of linguistic annotation data that includes language information, such as texts, tweets, and Wikipedia articles. This is where the PyNLPI library comes into play. It is an open-source library for natural language processing with Python. It comes with Python features for working with linguistic annotation data in the days of NLP.

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PyNLPI is an excellent toolkit for those interested in projects associated with advanced natural language processing with Python. It includes modules and packages for tasks such as n-gram extraction, frequency lists, and language model construction.

Advantages of PyNLPI:-

  • It is useful for both basic and advanced NLP techniques.

  • The modules and packages are simple to use.

Disadvantages of PyNLPI:-

  • It includes time-consuming linguistic annotation.

  • The processing and development times can be lengthy.

Summing up:-

I hope now you are more confident about your NLP project with the list of Python libraries. You are now well aware of the most trending Python NLP libraries, along with their advantages and disadvantages.

Choose the Python libraries for NLP wisely to get the maximum output from your project's work. In case you are new to NLP and want to dive into this field of endless opportunities, you can enroll in the Master’s degree in Data Science and AI Program. Here, you can opt for NLP-specialized artificial intelligence projects from various business domains.