Things To Know About Machine Learning Text Analysis 2022

Last Updated on September 16, 2022 by Muhammad Tabish

We live in an age where computers are in charge of everything. Try to do any job without a computer helping you. It will be much harder than it needs to be. The era of digitalization started when the first transistor was invented. The rest just followed.

A lot of people think that machine learning and text analysis software is a recent inventions. However, that couldn’t be further from the truth. People have wanted to speak with machines ever since they were first invented. However, the first trials were unsuccessful.

The software was preprogrammed with replies to the questions that could be asked. After a while, Noam Chomsky thought of a concept called universal grammar, and that could be applied to the English language. Essentially, every sentence can be constructed by using verbs, nouns, and adverbs.

Machine Learning Text Analysis

Objects, adjectives, and linking words can be included too, but the basis is quite simple. With a few rules, every sentence can be transformed into a tree of possibilities, with only a couple of them making grammatical sense.

This concept revolutionized natural language processing, and it has gained a lot of popularity over the years, seeing that now there are apps that can accurately predict the intent of a user. Another interesting thing is the Turing test. Basically, the Turing test is designed like this. You sit in front of a monitor, and you start a conversation online.

On the other side, there could be a person, or it could be a program. If you can’t distinguish whether it’s a real person or a computer, the program has passed it. Now, we’ll go a bit more in-depth with what goes through the mind of the application and how we can improve it in the future.

What’s The Purpose of Text Analysis?

If you’ve ever talked to Alexa, Siri, or Cortana, you probably haven’t thought that text analysis was included. Since you’re speaking to an object, it makes sense for the computer to just understand speech. However, speech is always transformed into text, analyzed, and then converted into audio again. Follow this link for more info.

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When you converse with Siri, for example, your voice gets recorded by the machine. Then it converts that audio file into text and processes all the data that has been gathered. If you’ve asked a question, Siri scans the internet for a solution and gives you the optimal answer.

There is a lot of machine learning that goes into the background, and billions of operations are calculated during those few seconds. After a certain timeframe, you get the response, and an audio file is played back to you. Another area where this is useful is text processing.

Microsoft Word is a perfect example. Whenever you have a typo in your text, there’s a red squiggly line below it to point it out. In the newer versions, certain typos get automatically corrected. This makes our lives easier.

How Can a Computer  Understand Us?

When a baby is born, it takes them a couple of years to grasp the concept of language. How is it then that a computer can understand text analysis machine learning and humans completely? Well, the answer is not that simple. Everything you see on electronic devices is just a combination of ones and zeros.

Plus, language is often ambiguous and imprecise. That’s why if you’re bilingual, you might have hated using Google Translate. That’s because an algorithm is oblivious to a metaphorical or double meaning of a word or a sentence. There are newer algorithms that try to discover the exact meaning of each phrase using large libraries of data.

This uses two different disciplines and a couple of different concepts. The first concept is grammatical structure. This includes the arrangement of words in a phrase and the following select rules. The first check is for the program to look through each piece and make segments.

Then, there’s part-of-speech. This determines which word is located at which place and what that could possibly mean. Next comes lexical semantics. This is closely correlated with meaning and interpretation. This is where named entity recognition comes into play and tries to select the context of the written piece.

What Does The Future Hold?

If you told a person from the 50s what we have today, they probably wouldn’t believe you. The rapid progress of technology from even a decade ago is gigantic. No one knows what the future holds, but some scientists are hoping to get closer to discovering general artificial intelligence.

This means that we’re going to create a sentient machine with the ability to be connected to the internet. There’s a lot of discussion about the ethics behind this, and there are tons of movies on how it could go wrong. But if we do it right, it might revolutionize our lives to a degree we aren’t aware of.

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1 thought on “Things To Know About Machine Learning Text Analysis 2022”

  1. Assalamualaikum sir g,
    I am really happy to see that you kept this blog alive while i was busy making my career in oil and gas industry for getting marry soon. hahah

    Soon inshaALLAH i’ll back with a good post


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