Best Python NLP Libraries for Advanced Language Processing

Top Python NLP Libraries in 2024

Lets see the top Python libraries for NLP that you should use in 2024.

 

Natural Language Toolkit

One of the most used libraries within NLP is called Natural Language Toolkit, NLTK. This is an open-source library that offers a lot of tools, including tokenization, stemming and lemmatization, among others. NLTK is used in the processing of text data on domains like sentiment analysis, topic modeling, and machine translation.

The library has all of the major fundamental features needed to do nearly every sort of Python natural language handling activity. It helps computers read and process words, sentences, and comprehend what they mean. It understands words and assists computers with figuring out stories and messages.

NLTK functionalities:

• Tokenization, POS, NER, classification, sentiment analysis, access to corpora

Pros: 

•  Most well-know and full NLP library with many 3rd extensions

•  Supports the largest number of languages compared to other libraries

Cons: 

•  Difficult to learn and use

•  Slow

•  Only splits text by sentences, without analyzing the semantic structure

TextBlob

TextBlob is the ideal entry-level NLP library, despite the fact that it may not be the most robust tool available and may not be sufficient for larger projects.

With an incredibly friendly UI, TextBlob assists developers with getting to know the world of NLP applications. Assuming you're searching for the best place to learn what noun phrase extraction or sentiment analysis even are, TextBlob is for you.

TextBlob Functionalities:

• Tokenization, POS, NER, classification, sentiment analysis, spellcheck, parsing

Pros:

•  Simplicity

•  Integration with NLTK and pattern

•  Ease of learning

•  Provides language translation and detection which is powered by google translate

Cons: 

•  Limited Complexity

•  No neural network models

CoreNLP

This NLP library is developed by the Stanford Natural Language Processing Group. The most significant advantage of CoreNLP is its high-speed and works well in product development environments. It's eminent for its robustness and supports different tasks, including named entity recognition and coreference goal.

CoreNLP Functionalities:

Tokenization, Part-of-Speech Tagging, Named Entity Recognition (NER), Sentiment Analysis, Dependency Parsing

Pros:

•  Wide Range of NLP Tasks

•  Integrated Pipeline

•  Multilingual Support

•  Pre-trained Models

•  Active Development

Cons: 

•  Resource Intensive

•  Dependency on Java

•  Limited Customization

spaCy

SpaCy is a cutting edge NLP library that gives quick and productive tokenization and parsing devices. It supports over 50 languages and gives pre-prepared models named entity recognition, dependency parsing, and more. SpaCy is known for its speed and precision, making it a popular choice for handling large datasets.

spaCy Functionalities:

Tokenization, POS, NER, classification, sentiment analysis, dependency parsing, word vectors

Pros: 

• Efficiency

• Pre-trained Models

• User-Friendly

• Support for Multiple Languages

Cons: 

• Customization Limitations

• Resource Intensive

Polyglot

Polyglot is a flexible, multilingual Python package developed for NLP tasks. It includes support and tools for a wide set of NLP tasks, from basic ones like tokenization and named entity recognition to part-of-speech tagging. Multilingual functionality is available in this library, making it useful in case of high linguistic diversity projects.

Polyglot Functionalities: 

• Tokenization, named Entity Recognition (NER), Part-of-Speech Tagging, Sentiment Analysis, Language Detection

Pros:

• Multilingual Support

• Generalized tools for NLP tasks

• Easy to integrate

Cons:

• Limited Documentation

• Performance Variability

• Less Community Resources

Gensim

Gensim is a Python library that recognizes semantic comparability between two documents through vector space modeling and point modeling toolkit. It can deal with large text corpora with the assistance of efficient data streaming and incremental algorithms, which is more than can be said about different packages that only target batch and in-memory processing.

Although it is not a complete NLP toolkit like NLTK or spaCy, it supports a variety of other NLP tasks and was initially developed for topic modeling. Its primary use case is working with word vectors.

Gensim Functionalities:

Text preprocessing, Document Representation, Word Embedding, Topic Modeling, Document Similarity and Retrieval

Pros: 

•  Efficiency and Scalability

•  Topic Modeling

•  Word Embeddings

Cons: 

•  Sparse Documentation

•  Limited Deep Learning Support

Pattern

Pattern is a Python library for machine learning and NLP. IIt is used for various NLP tasks, from tokenization to part-of-speech tagging, named entity recognition, and sentiment analysis, among other things. Pattern is a library for machine learning, text processing, natural language processing, web mining, and network analysis.

Pattern Functionalities:

• tokenization, POS, NER, sentiment analysis, parsing

Pros: 

•  Ease of Use

•  Integration with Other Libraries

•  Multilingual Support

•  Network analysis and visualization

Cons: 

•  Not optimized for some specific NLP tasks

•  Limited Documentation

•  Slow Development Activity

Hugging Face

Hugging Face is one of the top Python libraries in natural language processing, which provides a wide array of pre-trained models and tools to work on tasks such as text classification, sentiment analysis, and generation. With a user-friendly interface and highly effective potential, Hugging Face has made its mark as one of the top resources for all developers and researchers in NLP.

Hugging Face Functionalities: 

• Transformers Library, Tokenization, Pipeline API, Training and Fine-Tuning, Datasets Library

Pros:

• Wide Range of Pre-Trained Models

• Robust community support and regular updates with new features and improvements.

• Easily integrates with popular frameworks like PyTorch and TensorFlow.

Cons:

• Resource Intensive: 

• The wide array of options and configurations may be overwhelming for newcomers.

• Pre-trained models can be large, impacting download times and storage.