Natural Language Processing 101

Category: AI insights

Published date: 06.04.2020

Read time: 6 min

We all love to use a virtual personal assistant such as Alexa, Siri, Cortana, and many others, but have you ever wondered how these machines are able to understand various human languages? The answer is through a technology called Natural Language Processing (NLP). In this article, we will take a closer look at NLP to find out how researchers use it to train computers to interact with people on a day-to-day basis.

Natural Language Processing

What is Natural Language Processing?

Natural Language Processing is a branch of artificial intelligence that deals with communication between machines and humans using the regular language that we use every day. One of the most well-known achievements in NLP is IBM’s Watson. The reason this is a technological marvel is that Watson is not only capable of understanding human speech, but he is also able to detect the nuances that people use when they are talking with one another. For example, Watson can understand if somebody is being sarcastic, making a pun and many other speech patterns that are easy for humans to understand but difficult for machines.

How Does NLP Work?

In order to create something like Watson or even a simpler model, researchers have to apply algorithms to identify the standard and other rules that the language follows. This is necessary so that the unstructured language data can be transformed into a form that the computers can understand. In order to train the machine learning algorithms to understand the words, researchers have to feed many terabytes of data into the computer so that they will be able to understand human speech and distinguish certain keywords from the rest.

However, even before researchers can feed the text into the computer, it must be annotated so that the machine knows what it has to learn. This involves data annotators labeling the necessary words, speech patterns and anything else the computer must detect. Such work is very tedious and time-consuming which is why a lot of companies choose to outsource such jobs to companies like Mindy Support who have the necessary personnel to get the job done on-time.

Difficulties in NLP

When Google Translate first came out, the users were not happy with its performance because it provided a textbook translation of a word, even it was given a completely different meaning in the text. The reason it worked so poorly 14 years ago is because the AI and ML were not advanced enough to handle such advanced queries.

We often use words in ways that differ from their traditional meaning or usage. Even though the human brain can understand that this word is used in a special way, it can be difficult for computers to pick up. Researchers use POS tagging to assign each word a particular tag and then create a dependency graph of the entire sentence to teach the computer which words are the most important or are used out of the context. POS tagging is also something that can be outsourced given the vast amount of text and speech variations that must be fed into the machines.

There are also a lot of difficulties with the vocabulary. First of all, most NLP software applications do not end up creating a sophisticated vocabulary. There are also difficulties in linking together vocabulary terms. For example, if we take a sentence like “All employees within the company have a responsibility to conserve energy, with the ultimate accountability residing with Board. In this example, the words “Board” and “conserve energy” are connected since the sentence is discussing the level of accountability, but they are very statistically distant which makes it difficult for the computer to determine whether or not there is a relationship between these two terms.

The Future of NLP

Despite the difficulties mentioned above, NLP still has a bright future given the benefits that it offers. For example, it can increase the level of customer service by power a chatbot that can handle all sorts of customer inquiries. Businesses can also use NLP for sentiment analysis. If a company would like to know what the general public’s attitude is towards their business they can use NLP to collect various mentions of the company online and gauge the sentiment of all the comments collected. This can give companies powerful business insights that they can use to get a competitive advantage.

NLP is in use today to a certain extent with the virtual assistants in smartphones and other devices, but you can expect them to start being used to a greater extent as time goes on and the technology becomes more advanced. With the necessary researches and breakthroughs, you will be able to talk to a machine the same way you talk with any person.

Posted by Il’ya Dudkin


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