What is tokenization and how does it work?
Tokenization is the process of breaking up the original text into component pieces which are known as tokens. Tokens have a variety of useful…
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Browse 117 Natural Language Processing questions and answers from learners and industry experts. Find practical fixes, interview prep and step-by-step solutions - or ask your own.
Tokenization is the process of breaking up the original text into component pieces which are known as tokens. Tokens have a variety of useful…
1 Answer · 1.6K Views · Answered ✓
I am currently trying to explain a complex logic topic to someone who is new in natural language processing. How can I describe the concept of NER(…
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The above code works only in small size but crashes in large document. For solving this problem, we should not coerce the TDM to a matrix. That will…
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Before classification of text data, a text needs to be cleaned by following preprocessing steps Lowercasing Lowercasing is one of the simplest and…
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Text classification can be used in many cases in real world applications. Also, they are helpful in solving problems like time management and they…
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We will be performing a text classification technique on sms collection which contains number of spam and ham messages.Now we will check and remove…
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We can implement in R after importing following librariesWe will perform the following operation on the doc sentence.We get the following output in…
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Here, smsspamcollection is a dataset contains messages as spam and ham. We need to transform all the words into vectors. Text preprocessing,…
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Tf-idf or term frequency-inverse document frequency, is a measure of weight often used in information retrieval and text mining techniques. This…
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Update=False works but when Update is set to true following error occurs. How to fix that?The solutions could be training the model after the Update…
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There is a function from_pretrained() which makes loading an embedding very comfortable. Below is an example of the codeThe weights from gensim can…
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One-hot encoding is a method which vectorizes any categorical features. It can be done just by adding one to each category. It is simple and fast but…
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