Natural Language Processing (NLP) is an essential application under Artificial Intelligence (AI) that allows machines to process and respond to the spoken or written form of human languages. With the growing importance of NLP, businesses are adopting it to address numerous issues related to language.
It has changed the way most businesses relate to their customers–whether in sentiment analysis, machine translation, or even bots powered by NLP. In this article, we review the top 10 NLP Python libraries for NLP that will help in the effective and efficient NLP implementation.
It is also known as NLTK and is probably the most used library among all Python libraries intended to perform natural language processing tasks. It provides an easy-to-use interface to access more than 50 corpora and lexicons such as WordNet.
There are also modules for text categorization and classification, tokenization, lemmatization, parsing, and semantic analysis. The primary use of NLTK is for research and teaching purposes but it has some production issues due to its speed of processing which is unfit for use in production systems.
Key Features:
Tokenization and stemming
Named Entity Recognition (NER)
Text classification and sentiment analysis
Semantic reasoning
Cons:
It can be slow and less optimal for production environments.
SpaCy is a state-of-the-art NLP library that is built for the fast implementation and use of natural language processing tasks. The clever and efficient architecture means large data sets can utilize large portions of the CPU and RAM quite rapidly. SpaCy provides support for tokenization, part-of-speech (POS) tagging, NER, dependency parsing, and over 49 language tasks.
Key Features:
Fast and efficient, written in Cython
Supports transformers like BERT
NER and POS tagged with tokenization in more than one language
Deployment models with pre-trained versions
Pros:
SpaCy is much faster than many other libraries, making it suitable for large-scale projects.
Gensim is a remarkable Python library for document similarity computation and topic modeling. It is lightweight yet designed for handling huge text datasets while saving up on memory. Additionally, Gensim applies methods such as Latent Semantic Analysis (LSA) and deep learning architectures like word2vec.
Key features:
Memory efficient and makes it possible to work with large datasets
Computation of topic models via LDA and HDP
Computation of text similarity and document processing
Advantages:
Capable of processing using low resources.
TextBlob is another simple and user-friendly Python library that is an enhancement of the NLTK tool. It does this very well by presenting functions that analyze and visualize the sentiments, tagging parts of speech, and extracting important noun phrases. TextBlob is also suitable for intermediate-level users because it has a straightforward interface and good usability.
Key Features:
Sentiment detection
Text classification
Text extraction, especially noun phrases
Integrates nicely with NLTK
Pros:
Designed for neophytes, facilitates learning and small projects.
Stanford CoreNLP is the language processing toolset provided by Stanford University. It includes functionalities like POS, NER, parsing, or even sentiment analysis. Even though it is developed in Java, CoreNLP provides a wrapper in Python for the convenience of the developers using Python.
CoreNLP Features:
Tools for NLP such as coreference resolution, parsing, etc.
Multi-language support including English, Chinese, French, etc.
An in-depth understanding of the language is supported
Cons:
Java dependency increases the overhead.
AllenNLP is a recently developed library for NLP research and it is based on PyTorch. It is meant for both research and deployment. The rising need for such architecture is because AllenNLP simplifies building deep learning models for applications like coreference resolution and semantic role labeling. It provides pre-built models, making it suitable for quick model creation.
Features:
NLP models with deep learning enhancements
NLP task-specific pre-trained models
User-friendly interface to work with even hard tasks
Advantages:
Proves useful for AI researchers and developers who wish to explore the training and implementation of various NLP structures and models.
Polyglot may not be as popular as other libraries but provides amazing capabilities for performing multilingual NLP tasks. There are great features of language detection, NER, and sentiment analysis supporting various languages which make the use of this library remarkable. One of its major attraction points is that Polyglot can handle 196 languages which makes it very useful and appealing for industries with international projects.
Key Features:
Language detection and tokenization in 196 languages
Named Entity Recognition (NER)
Sentiment analysis in 136 languages
Pros:
Very fit for several multilingual NLP tasks.
Scikit-learn as a library for machine learning, is also adopted for many NLP tasks since it has a plethora of algorithms. There are also available ways in Scikit-learn to represent text as vectors in numerical format and it performs perfectly for classification and regression of data sets containing text.
Key Features:
Text vectorization using bag-of-words or TF-IDF
It is commonly used in the making of machine-learning pipelines
Includes classification, clustering, and regression.
Cons:
No support for deep learning architectures by default; it can be used in conjunction with other deep learning frameworks.
An exciting aspect is the library provided by Hugging Face which enables the implementation of cutting-edge deep learning-based models for NLP. Indeed, it provides already trained models such as BERT, GPT, and RoBERTa that can be used for various applications, such as answering questions, text categorization, and translation systems. Hugging Face makes it easier to embed transformers in any NLP pipeline.
Featured Highlights:
BERT, GPT-3, RoBERTa, and other pre-trained models.
A rich repository of over 10,000 models.
An interface that eases text generation, machine translation, and other processes.
Advantages:
Well suited both for research purposes and implementing natural language processing solutions in production.
Flair is unlike most libraries focusing on natural language processing as it was born at Zalando Research which is an internet fashion retailer. This online shop aims to provide all users with the most advanced tools related to natural language processing including contextual word representations such as ELMo, BERT, or, as recently introduced, GPT. Apart from that, it has a lightweight wrapper for problems such as NER or POS tagging, text classification, and so on.
Key Features:
Offers word embeddings implementation for BERT and ELMo languages
Clearly-structured and nice-looking API for NER and text classification
Pre-trained models can be downloaded in many languages
Advantages:
You can never go wrong with Flair, for it has all possible features of word embeddings which make it suitable even for most sophisticated language processing strategies.
For experienced NLP specialists and novices alike, the top 10 Python libraries discussed above each have unique characteristics designed for a range of NLP tasks. From performing topic modeling in Gensim to training transformer models with Hugging Face, these libraries will enable the users to process, analyze, and build text data model quickly and efficiently. Choose the most suitable library for your project and explore the exciting possibilities of NLP.
Beginners can easily work with NLTK and TextBlob.
The performance level is commendable with real-life applications for SpaCy, Gensim, and CoreNLP.
Hugging Face and AllenNLP have more advanced models for attention-grabbing NLP tasks.