{"id":452,"date":"2020-08-18T09:07:31","date_gmt":"2020-08-18T09:07:31","guid":{"rendered":"https:\/\/data-science.gotoauthority.com\/2020\/08\/18\/most-popular-word-embedding-techniques-in-nlp\/"},"modified":"2020-08-18T09:07:31","modified_gmt":"2020-08-18T09:07:31","slug":"most-popular-word-embedding-techniques-in-nlp","status":"publish","type":"post","link":"https:\/\/wealthrevelation.com\/data-science\/2020\/08\/18\/most-popular-word-embedding-techniques-in-nlp\/","title":{"rendered":"Most Popular Word Embedding Techniques In NLP"},"content":{"rendered":"
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To build any model in machine learning<\/a> or deep learning, the final level data has to be in numerical form, because models don\u2019t understand text or image data directly like humans do.<\/p>\n

So how natural language processing<\/a> (NLP) models learn patterns from text data \"\ud83e\udd14\"<\/p>\n

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We need smart ways to convert the text data into numerical data, which is called vectorization<\/strong> or in the NLP world, it is called word embeddings.<\/strong>\u00a0<\/p>\n

Vectorization or word embedding is nothing but the process of converting text data to numerical vectors. Later the numerical vectors are used to build various machine learning models. In a way, we say this as extracting features<\/strong> from text to build multiple natural language processing models.<\/p>\n

We have numerous ways to convert the text data to numerical vectors. In this article, we will see details about different word embedding techniques with examples, and also we will learn how to implement them in python.<\/p>\n<\/div>\n

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Most popular word embedding techniques in natural language processing<\/p>\n<\/div>\n

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Before we dive further, let\u2019s quickly see what you will learn in this blog post.<\/p>\n<\/div>\n

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Natural Language Processing(NLP)<\/h2>\n

Natural Language Processing, in short, called NLP, is a subfield of data science<\/a>. With the increase in capturing text data, we need the best methods to extract meaningful information from text. For this, we are having a separate subfield in data science and called Natural Language Processing. Using these natural language processing techniques we build text-related applications or to automate tasks.<\/p>\n

In technical words, Natural Language Processing is the process of training machines to understand and generate results like humans using our natural languages. Based on these 2 tasks, NLP is further classified as<\/p>\n