Natural Language Processing In a Nutshell
As humans, one of the things which makes us different from other animals is how well we communicate with each other. To do that, we use words and language. We read with words, we write with words and we speak with words. Even we plan dream and understand the world around us in terms of words.
In a way, if we can unlock the key to understanding how language works, we unlock the key to understand how the human brain works.
So, natural language processing (NLP) is an amazing tool for that. NLP combines the power of artificial intelligence, linguistics, and computer science and allows the machine to understand how language works.
How natural language processing works
Natural language processing first started with the work of Tomas Mikolov. Mikolov asked if the word “man” is related to the word “woman”, word “king” is related to what? Did you guess? Yes, queen.
We can basically reason about that in terms of meaning(king)- meaning(man) +meaning(woman) = meaning(queen).
To understand how machines think better, let's look at how we think first. So in our brains, we have representations of the words and we also know that similar words live somewhere close together. We replicate that as mathematical structures and recreate that as a multidimensional space. That’s basically what machines are doing.
So how machines solve Mikolov’s problem is by vectors in multidimensional space: vector(king)- vector(man)+ vector(woman)= vector(queen)
By using that understanding among words, they can perform assorted tasks.
Natural language processing applications
In some way, NLP is the automatic handling of natural human language. And even though you may not be realized you probably used one of the NLP tools like translating one language to another or auto word corrector. Let’s look at the examples:
Machine Translation
Machine translation (MT) is essentially translating one human language to another. Maybe machine translation became a natural feature to you by now with Google translate or the globe icon on Twitter, but it is not easy as it seems.
We said earlier that machines understand human language by vectors. Now, it can understand all the languages by vectors and while translating, the machine is trying to bring closer each vector (word) so that they have the same meaning but in a different language.
Aside from how cool it is, it’s also pretty useful. Because MT works really well when it comes to restricted domains. Such as technical manuals or specific catalogs.
Of course with breakthrough studies the last couple of years, MT is developing pretty fast and even the domain is not restricted it works better each day.
Text Classification
Text classification’s main goal is to tag the data. For example in some email apps, there is an auto spam detection future. Auto spam detection tags the mails eighter spam or ham before it gets your inbox. These kinds of two-tag classifications are called binary classification.
There is also multi-class classification. Such classifiers can organize data with multiple tags. For instance, let’s take this article. If we wanted to tag this article we could use classification, natural language processing, and AI tags. So what would the classifier do? It depends on the application but, most likely it would candidate the tags. But of course, in some cases, it may make sense to assign several tags to the data.
Sentimental Analysis
Sentimental Analysis is one of the best parts of natural language processing because it gives really useful insights. Especially institutions and corporations use it for checking out what their customers or general public feel about them.
Polarity detection is the most used kind of sentimental analysis. It detects if the given text is negative, positive, or neutral.
If the given text is straightforward, then it is simple to perform the task. It gets complicated if there is irony or the word is synonyms.
For example, if the text is “I loved Tarantino’s new movie. It’s an immersive movie.” it is not hard to perform the analysis.
But what if the text is “I loved Tarantino’s new movie. Great for a Sunday nap.” then it gets complicated. Even the words are clear, it causes serious issues.
Text Summarization
Text summarization is highly useful in today’s digital world. Think of a day that you want to read an article. Do you just start to read from the first page to the end or do you take a look at the summary to understand if it is for you?
You probably first read the summary. This applies to the white papers, articles, or news too.
Extractive text summarization is the traditional way, which process is to identify significant phrases, sentences in your text. With this method, the summary will contain the most important parts of your text.
The other method is abstraction-based text summarization. In this method summarizer does not copy the sentences from the text input, instead, it creates its own sentences. But it is not widely used right now, because it is extremely complex.
Personal thoughts
Natural language processing is one of the areas that stir me up. It is so promising and developing area. Maybe someday we’ll see machines that pass the Turing test thanks to NLP.
In AIX, we see NLP as a field that will shape the future of technology and science. NLP will help us understand the human brain better and enable us to produce a complex machine “brain”.
We are working to make NLP and AI-related technologies accessible. You can visit our website to check out what we are doing and if you want to use our NLP service when it launched you can sign up for our newsletter.
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