The Essential NLP Guide for data scientists (codes for top 10 NLP tasks)

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Technologies that can make a coherent summary take into account variables such as length, writing style and syntax.Automatic data summarization is part of machine learning and data mining. The main idea of summarization is to find a subset of data which contains the information of the entire set. Such techniques are widely used in industry today. Search engines are an example; others include summarization of documents, image collections and videos. Document summarization tries to create a representative summary or abstract of the entire document, by finding the most informative sentences, while in image summarization the system finds the most representative and important (i.e.


A Guide to Natural Language Processing - Federico Tomassetti - Software Architect

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Natural Language Processing (NLP) comprises a set of techniques that can be used to achieve many different objectives. Take a look at the following table to figure out which technique can solve your particular problem.


Introduction to NLP โ€“ Towards Data Science

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Natural language processing (NLP) is an area of computer science and artificial intelligence that is concerned with the interaction between computers and humans in natural language. It is the driving force behind things like virtual assistants, speech recognition, sentiment analysis, automatic text summarization, machine translation and much more. In this post, you will learn the basics of natural language processing, dive into some of its techniques and also learn how NLP benefited from the recent advances in Deep Learning. Natural Language Processing (NLP) is the intersection of Computer Science, Linguistics and Machine Learning that is concerned with the communication between computers and humans in natural language. NLP is all about enabling computers to understand and generate human language. Applications of NLP techniques are Voice Assistants like Alexa and Siri but also things like Machine Translation and text-filtering.


Ultimate Guide to Understand & Implement Natural Language Processing

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According to industry estimates, only 21% of the available data is present in structured form. Data is being generated as we speak, as we tweet, as we send messages on Whatsapp and in various other activities. Majority of this data exists in the textual form, which is highly unstructured in nature. Few notorious examples include โ€“ tweets / posts on social media, user to user chat conversations, news, blogs and articles, product or services reviews and patient records in the healthcare sector. A few more recent ones includes chatbots and other voice driven bots. Despite having high dimension data, the information present in it is not directly accessible unless it is processed (read and understood) manually or analyzed by an automated system.