Besides, NLP is widely applied in creating word processing applications and translation software. Not only translation machines, but even search engines or banking apps and chatbots are being developed with the use of NLP, which enhances systems in a way that humans' speech and writing can be excelled for better understanding.
Gensim is a high-speed Python library scaled mainly for topic modelling tasks, such as recognition of text similarities and ways to get around the numerous documents, index texts. Among the chief advantages of using gensim are the possibilities for treatment of huge volumes of data.
2. SpaCy
Processing SpaCy is one of the newest open-source libraries for NLP. It is a Python library that is very fast and has good documentation. This library supports giant datasets. Apart from that, this library provides the user with a number of pre-trained NLP models. SpaCy targets those users who are preparing text either for deep learning or extraction.
3. IBM Watson
All AI-based services are stored within the IBM cloud and offer a variety of services to users. This is can be considered as a versatile suite, when performing Natural Language Understanding tasks in the identification of keywords, emotions, and categories. The versatility provided by IBM Watson lends itself easily for use in a wide range of industries, from healthcare to finance.
4. Natural Language Toolkit (NLTK)
It allows users to build Python programs to work with human language data. NLTK offers easy-to-use interfaces to more than 50 corpora and lexical resources, besides several text processing libraries. Other resources include an active discussion list. Interests represented in this free, open-source platform include educators, students, linguists, engineers, and researchers.
5. MonkeyLearn
MonkeyLearn is a fully AI-powered NLP platform that enables its users to extract insights from text data. It's a user-friendly platform with pre-trained models in topic classification, keyword extraction, sentiment analysis, and other customized machine learning models, not to mention changing them to suit various business needs. It can be integrated into apps such as Excel and Google Sheets for executing text analysis.
6. TextBlob
TextBlob is a Python library and, in some sense, an extension of NLTK. This interface makes it easy for beginners to implement part-of-speech tagging, text classification, sentiment analysis, and many more facilities through the easiness of the interface. It is also more user-friendly than the rest of the libraries for those people who are new to NLP.
7. Stanford Core NLP
Stanford Core NLP was developed and is now maintained by the group at Stanford University involved in NLP. This library, written in Java, requires the user to first install the Java Development Kit in their PC. It offers APIs almost in all programming languages and is appropriate for executing tasks such as tokenization, named entity recognition, and part-of-speech tagging. As Core NLP provides scalability and speeds optimization, it works well for performing complicated tasks.
8. Google Cloud Natural Language API
The Google Cloud Natural Language API belongs to the suite of services brought by Google Cloud and has integrated question answering and language understanding technologies. This interface offers several pre-trained models to a user for performing entity extraction, content classification, and sentiment analysis.
9. FlaIR
FlaIR is a very powerful NLP library developed by Zalando's Research team. It has been built on PyTorch, offering a clean, simple API to accomplish different NLP tasks.
It consists of pre-trained models for a sequence-labelling task, like named entity recognition. Besides, it also supports contextual string embeddings, along with that it has easy-to-use API (Application Programming Interface) with very little boilerplate code. Its benefits include high accuracy for sequence labelling, its models are flexible and can be extended, active development and community support.
It supports named Entity Recognition in biomedical texts, document categorization through text classification, and chatbots that undertake sequence labelling.
10. FastText
FastText is a Facebook Artificial Intelligence Research library that provides an efficient way for representing and classifying texts. FastText has fame in large datasets processing due to its speed and scalability.
The library provides two functionalities: text classification and learning of word vectors, including very fast training of text classification on large datasets. Besides, it has pre-trained models for 294 languages.
Its benefits include large-scale text classification, efficient light and easy deployment in any production environment, and can be easily used with a very simple command-line interface. It supports multilingual text classification, word embeddings for NLP Applications and language identification in multilingual corpora.
