I have covered several topics around NLP in my books “Text Analytics with Python” (I’m writing a revised version of this soon) and “Practical Machine Learning with Python”. However, after working as a Data Scientist on several challenging problems around NLP over the years, I’ve noticed certain interesting aspects, including techniques, strategies and workflows which can be leveraged to solve a wide variety of problems. When I started delving into the world of data science, even I was overwhelmed by the challenges in analyzing and modeling on text data. And people usually tend to focus more on machine learning or statistical learning. Hence, often it is perceived as a niche area to work on. It is primarily concerned with designing and building applications and systems that enable interaction between machines and natural languages that have been evolved for use by humans. Motivationįormally, NLP is a specialized field of computer science and artificial intelligence with roots in computational linguistics.
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This should give you a good idea of how to get started with analyzing syntax and semantics in text corpora. This article will be covering the following aspects of NLP in detail with hands-on examples. A lot of these articles will showcase tips and strategies which have worked well in real-world scenarios.
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