Natural Language Processing is a branch of artificial intelligence designed to understand the semantics and implications of human languages. The core functionality of NLP is to collect information from text, then later use the information to train data models. Some of the NLP tasks are text mining, text analysis, text classification, word sequencing, speech recognition, and lastly synthesis.

The development of NLP has been a continuous process that has led to NLP implementation in a wide range of industries worldwide. As the system is now part of deep learning research, developing chatbots, patent research, voice & speech recognition, searching picture content, and other uses. In short, NLP is designed to make text preprocessing much simpler and easier.

What to look for in an NLP library?

Here are some of the points that make the NLP library decent:

•   Transform free-text phrases into structured characteristics
•   Easy-to-learn API model
•   Apply the best algorithm

The NLP library mentioned below will carry all the above points to ensure to give you the best performance compared to others in the market.

Hugging Face Transformers (57.1 Github stars) Updated

It provides:

•   Thousands of pre-trained models to perform tasks on different media such as text, vision, and audio.
•   The models here can be applied to text, images, and audio.
•   It can also perform tasks with several modalities combined such as table question answering, video classification, visual question answering, and optical character recognition.

spaCy (22.2k GitHub stars) Updated

Key points:

•   Free open-source library for NLP in python and Cython
•   It was designed using production environments
•   COmes with pre-trained pipelines and supports training for 60+ languages.
•   It can be used for text classification, multi-task learning with pre-trained transformers, production-ready training systems, easy model packing, deployment, and workflow management.

Fairseq (15.1GitHub starts) Updated

Key points:

•   You can create custom models for translation, summarization, language modeling, and other text generation tasks with the Fairseq tool kit.
•   Fairseq also offers reference implementation of different sequence modeling papers.

Jina (13.9k GitHub stars) Updated

Key Points:

•   It is a neural search framework designed to build neural search applications in mere minutes.
•   Jina can help with indexing, querying, and comprehending multi cross model data types such as video, image, text, source code, PDF, and audio.

Gensim (12.8k GitHub stars) Updated

Major points:

•   It is a python library for document indexing, topic modeling, and similarity retrieval.
•   It offers multicore implementations of popular algorithms such as Latent Dirichlet Allocation, Random Projections, Hierarchical Dirichlet process, and Online Latent Semantic Analysis.

Flair (11.2 GitHub stars) Updated

Major points:

•   It allows you to apply NLP models to your text, supports biomedical data, and sense disambiguation and classification.
•   Flair language supports are in the groin state.
•   The interface here is rather simple and allows you to merge different worlds and embeddings.
•   It is much easier to train models and experiment with them with Flair, as the framework of Flair is built on PyTorch.

AllenNLP (10.8k GitHub Stars) Updated

Major points:

•   It is also built on PyTorch and can handle a wide variety of linguistic tasks.
•   AllenNLP comes with a broad range of existing model implementations, and high-level configuration language for implementing NLP common approaches.
•   Implementation of common approaches allows the experimentation of a broad range of tasks through configurations.

NLTK (10.4 GitHub Stars) Updated

Major points:

•   It is an open-source python module, data sets, and tutorials, all dedicated to the research and development of NLP.
•   The highlight of NLTK is the easy-to-use interface that supports over 50 corpora and lexical resources.
•   It also supports text processing libraries of classification, tokenization, stemming, tagging, parsing, and wrappers for industrial-strength NLP libraries.

CoreNLP (8.3k GitHub Stars) Updated

Major points:

•   It offers natural language analysis tools all written in Java.
•   CoreNLP can handle raw human language text inputs and offer base forms of words, normalize and interpret dates, times, parts of speech, people, etc.

Pattern (8.1k GitHub stars) Updated 2 years ago

Major points:

•   It is a web mining module for Python.
•   The pattern comes with tools for data mining for web services, a web crawler, HTML DOM parser.
•   It also offers NLP models such as part of speech taggers, sentiment analysis, wordnet, and n-gram search.
•   The pattern also implements machine learning models such as clustering, classifications, and vector space models.

TextBlob (8k GitHub Stars) Updated

Major points:

•   It is a python library for textual data.
•   TextBlob offers a simplistic API for common natural language.
•   It supports NLP tasks such as noun phrase extraction, translations, sentiment analysis, classifications, and more.

Hugging Face Tokenizers (5.2k GitHub stars) Updated

Major points:

•   Hugging face tokenizers are more focused on performance and versatility.
•   It supports the most used tokenizer in today’s world.

Haystack (3.8 Github stars) Updated

Major points:

•   Haystack helps you build powerful and production-ready pipelines for different search use cases.
•   Haystack comes with state-of-the-art NLP models for unique search experiences and queries in natural languages
•   It also covers the best technology that can be found on open-source projects.

Snips NLU(3.6k GitHub stars) 2 years ago updated

Major points:

•   Another addition to the python library that allows the extraction of structured information from natural language sentences.
•   The sentences can get easily translated into machine-readable descriptions.

NLP Architect (2.8 GitHub Stars) Updated

Major points:

•   It is an open-source python library for learning topologies and optimization of NLP and NLP neural networks.
•   It is designed to be more flexible and rapid integration of NLP models.

Rosetta (420 Github Stars) Updated

Major points:

•   It is based on tensor flow and acts as a privacy-preserving framework.
•   It can easily integrate with mainstream privacy-preserving computation technologies such as cryptos, federated learning, and a trusted execution environment.
•   Rosetta adopts the API of TensorFlow, so you can expect to transfer traditional TensorFlow codes into a privacy-preserving manner with some minor changes.

Conclusions

These are some of the best NLP libraries available in the market, but if you need quick and professional implementation of the ML technology for your business case, the most effective is to invite an expert software development company. Consult with Serokell about any of your AI-related projects, I was in touch with them about my case, and was very satisfied with the results. I am sure they could help you too.