This library was developed at Stanford University and it’s written in Java. However, it also inherited the main flaws of NLTK – it’s just too slow to help developers who face the demands of NLP Python production usage. It’s very helpful in designing prototypes. We believe anyone who wants to make their first steps toward NLP with Python should use this library. It basically provides beginners with an easy interface to help them learn most basic NLP tasks like sentiment analysis, pos-tagging, or noun phrase extraction. TextBlob is a must for developers who are starting their journey with NLP in Python and want to make the most of their first encounter with NLTK. The learning curve is steep, but developers can take advantage of resources like this helpful book to learn more about the concepts behind the language processing tasks this toolkit supports. NLTK can be rather slow and doesn’t match the demands of quick-paced production usage. This library is pretty versatile, but we must admit that it’s also quite difficult to use for Natural Language Processing with Python. Many universities around the globe now use NLTK, Python libraries, and other tools in their courses. The library was developed by Steven Bird and Edward Loper at the University of Pennsylvania and played a key role in breakthrough NLP research. Today it serves as an educational foundation for Python developers who are dipping their toes in this field (and machine learning). It's basically your main tool for natural language processing and machine learning. NLTK is an essential library supports tasks such as classification, stemming, tagging, parsing, semantic reasoning, and tokenization in Python. It provides developers with an extensive collection of NLP tools and libraries that enable developers to handle a great number of NLP-related tasks such as document classification, topic modeling, part-of-speech (POS) tagging, word vectors, and sentiment analysis. Moreover, developers can enjoy excellent support for integration with other languages and tools that come in handy for techniques like machine learning.īut there's something else about this versatile language that makes is such a great technology for helping machines process natural languages.
The simple syntax and transparent semantics of this language make it an excellent choice for projects that include Natural Language Processing tasks. There are many things about Python that make it a really good programming language choice for an NLP project.
Why use Python for Natural Language Processing (NLP)?
Read on to learn more 8 amazing Python Natural Language Processing libraries that have over the years helped us deliver quality projects to our clients. There are many tools and libraries created to solve NLP problems. Now, developers can use ready-made tools that simplify text preprocessing so that they can concentrate on building machine learning models. In the past, only experts could be part of natural language processing projects that required superior knowledge of mathematics, machine learning, and linguistics. Since NLP relies on advanced computational skills, developers need the best available tools that help to make the most of NLP approaches and algorithms for creating services that can handle natural languages. There’s a reason why tech giants like Google, Amazon, or Facebook are pouring millions of dollars into this line of research to power their chatbots, virtual assistants, recommendation engines, and other solutions powered by machine learning. This is also why machine learning is often part of NLP projects.īut why are so many organizations interested in NLP these days? Primarily because these technologies can provide them with a broad range valuable insights and solutions that address language-related problems consumers might experience when interacting with a product.
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Natural language processing (NLP) is a field located at the intersection of data science and Artificial Intelligence (AI) that – when boiled down to the basics – is all about teaching machines how to understand human languages and extract meaning from text.
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