AUG 08, 2023
What is Natural Language Processing and how does it work?
SEP 03, 2021
How does Siri or Alexa understand what you're saying? How can the computer translate your voice perfectly?
Have you ever wondered how virtual assistants like Siri and Cortana work? How do they understand what you're saying?
Well, part of the answer is natural language processing. This interesting field of artificial intelligence has led to some huge breakthroughs over the last few years, but how exactly does it work?
Read on to learn more about natural language processing, how it works, and how it’s being used to make our lives more convenient.
Natural Language Processing, or NLP, is how computers can understand human languages. For example, when you speak to voice-activated virtual assistants like Alexa or Siri, they listen, understand your speech, and perform an action based on what you’ve said.
Traditionally, humans could only communicate with computers via the programming language they were coded via particular commands. Code is inherently structured and logical, and the same commands will always produce the same output.
In contrast, human language is unstructured and much more complex. The same word or sentence can have multiple meanings based on inflections and context. And, there are many different languages.
So how is AI able to understand what we’re saying?
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NLP is trained with machine learning. Machine learning is a branch of artificial intelligence that takes large amounts of data into an algorithm that trains itself to produce accurate predictions. The more data and time the algorithm has, the better it gets. This is why NLP machines are so much better today than they were ten years ago.
NLP works via preprocessing the text and then running it through the machine learning-trained algorithm.
Here are four of the common preprocessing steps that an NLP machine will use.
Stemming is a simpler process and involves removing any affixes from a word. Affixes are additions to the start and end of the word that gives it a slightly different meaning. However, stemming can result in errors when similar words have different roots. Consider the words “camel” and “came.” Stemming may reduce “camel” to “came" despite having completely different meanings.
Lemmatization is much more complicated and accurate. It involves reducing a word to their lemma, which is the base form of a word (as found in the dictionary). Lemmatization takes into account the context and is based on vocabulary and morphological analysis of words. A good example is “caring.” Stemming may reduce “caring” to “car,” whereas lemmatization will accurately reduce it to “care.”
Another technique works alongside both processes, known as Stop Word Removal. This is the simple removal of words that add no relevant information to the meaning of the speech, such as “at” and “a.”
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Once the text has been preprocessed, an NLP machine is able to do several things depending on its intent.
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Natural Language Processing is a huge and ever-growing field that encompasses many functions. Some of the major uses of NLP are:
Natural Language Processing is changing the way we communicate with robots and how they communicate with us. Bloomberg News uses an AI system called Cyborg to produce almost a third of its content. Meanwhile, Forbes, The Guardian, and The Washington Post all use AI to write news articles.
And all of this is only possible thanks to NLP!