Top 10 Applications of Natural Language Processing (NLP)

Words, words, words… have you ever thought about how important they are? Communications, books, messages, telephone conversations, songs, movies… it is hard to imagine our world without language, isn’t it?

Just think about how many text and voice data we face every day. What about deriving meaning from this data and do something cool? Now we have systems that can do additional functions with our language. These systems are based on NLP — Natural Language Processing — the mixture of artificial intelligence and computational linguistics.

If it seems you have never encountered NLP, just open Google, click on access to voice match and say: “Ok, Google …” (other examples — Siri from Apple, Cortana from Microsoft). You will get needed information based on your voice request and all this due to the ability of NLP-based devices to understand the human language.

So, NLP is the machine’s ability to process what was said, structure the information received, determine the necessary response and respond in a language that we understand. So, how does NLP work, and what is NLP used for? I think everyone should be well-oriented in questions like this and for this reason, I made this post full of useful info.

Without further ado, let’s talk science!

How Does Computer Understand Text?

What do words and phrases mean to a computer, which can only understand zeroes and ones? It may seem not an easy task to teach machines to understand our communication. Well, yes and no. In a nutshell, the process of machine understanding using natural language processing algorithms looks like this:

1. A person says something to the machine.

2. The machine records sound.

3. The machine turns audio into text.

4. The NLP system parses the text into components, understands the context of the conversation and the intention of the person.

5. Based on the results of the NLP, the machine determines which command should be executed.

In short, it’s a process of creating algorithms that transform the text into words labeling them based on the position and function of the words in the sentence. For this, word embedding is a silver bullet to resolve many NLP problems. It transforms human language meaningfully into a numerical form. This allows computers to understand the nuances implicitly encoded into our languages.

The main idea here is every word can be converted to a set of numbers — an N-dimensional vector that stores information about the word’s meaning. Although every word gets assigned a unique vector/embedding, similar words end up having values closer to each other. For example, the vectors for the words ‘Man’ and ‘Boy’ would have a higher similarity than the vectors for ‘Boy’ and ‘Lion’.

Its goal is twofold: to improve other NLP tasks, such as machine translation, or to analyze similarities between words and groups of words. Of course, everything works well if the task is simple and straightforward. However, human speech is significantly different from the speech of a robot. The main difficulty for developers is the machine takes everything literally. Our language is very saturated and filled with poly-semantic words and hidden meanings.

Top 10 Applications of Natural Language Processing

What tasks can be solved with NLP? The scope is great and every day the number of tasks is increasing. Here are the most popular applications of NLP:

1. Machine Translation

Everyone knows what is a manual translation — we translate information from one language into another. When the same thing is done by a machine, we deal with “Machine” Translation. The idea behind MT is simple — to develop computer algorithms to allow automatical translation without any human intervention. The best-known application is probably Google Translate.

Google translate is based on SMT — statistical machine translation. It is not the work of word-for-word replacement alone. Google translate gathers as much text as it can find that seems to be parallel between two languages, and then it crunches data to find the likelihood that something in Language. And this is similar to us human, when we were children, we begin to assign semantic value to words, and abstract and extrapolate these semantic values given combinations of words.

But all that glitters is not gold and Machine translation is challenging given the inherent ambiguity and flexibility of human language. While human cognitive processes language interpretation or understanding, and translation on many levels, a machine processes data, linguistic form and structure, not meaning and sense.

2. Speech Recognition

Did you know that voice recognition technology has been around for 50 years? For half a century, scientists have been solving this problem, and only in the last few decades, NLP allowed to achieve significant success. Now we have a whole variety of speech recognition software programs that allow us to decode the human voice. It is a mobile telephony, home automation, hands-free computing, virtual assistance, video games, and etc.

All-in-all, this technology is being used to replace other methods of input like typing, clicking, or selecting text in any other way. Today, speech recognition is a hot topic that is part of a large number of products, for example, voice assistants (Cortana, Google Assistant, Siri, …). Everyone knows these apps are not so perfect. With a more complex task, NLP and neural networks do not cope well with their tasks.

But who knows, maybe this problem will be solved with time?

3. Sentiment Analysis

Sentiment analysis (also known as opinion mining or emotion AI) is an interesting type of data mining that measures the inclination of people’s opinions. The task of this analysis is to identify subjective information in the text. For example, this can be a movie review or an emotional state caused by this movie. Why do we need this? Companies use sentiment analysis to keep abreast of their reputation.

Sentiment analysis helps to check whether customers are satisfied with goods or services. Classical polls have long faded into the background. Even those who want to support brands or political candidates are not always ready to spend time filling out questionnaires. However, people willingly share their opinions on social networks. The search for negative texts and the identification of the main complaints significantly helps to change concepts, improve products and advertising, as well as reduce the level of dissatisfaction. In turn, explicit positive reviews increase ratings and demand.

4. Question Answering

Question answering (QA) is concerned with building systems that automatically answer questions posed by humans in a natural language. Sounds complicated? Well then here are the real examples of Question-Answering applications: Siri, OK Google, chat boxes and virtual assistants. I know that I have already mentioned these apps. But here is the point — all of them have a few NLP-applications or functions — to understand speech is only half of the path and another one naturally is to give a response.

5. Automatic Summarization

Going back to the amount of text data we face every day, information overload could be a real drawback but now we have Automatic Summarization. This is the process of creating a short, accurate, and fluent summary of a longer text document. The most important advantage of using a summary is it reduces the reading time. Here are some of the APIs you can try: Aylien Text Analysis, MeaningCloud Summarization, ML Analyzer, Summarize Text, Text Summary.

6. Chatbots

The first chatbots appeared in the 1960s, they were quite primitive: they basically rephrased what person spoke to them. Modern chatbots are not far from their ancestors. NLP has become the basis for creating chatbots, and although such systems are not so perfect they easily can handle standard tasks. Chatbots currently operate on several channels, including the Internet, applications, and messaging platforms. Businesses today are interested in developing bots that can not only understand a person but also communicate with him at one level. The latter, in truth, does not always work.

7. Market Intelligence

Marketers also use NLP to search for people with a likely or explicit intention to make a purchase. Behavior on the Internet, maintaining pages on social networks and queries to search engines provide a lot of useful unstructured customer data. Selling the right ad for internet users allows Google to make the most of its revenue. Advertisers pay Google every time a visitor clicks on an ad. A click can cost anywhere from a few cents to more than $ 50.

At its core, market intelligence uses multiple sources of information to create a broad picture of the company’s existing market, customers, problems, competition, and growth potential for new products and services. Sources of raw data for that analysis include sales logs, surveys, and social media, among many others.

8. Text Classification

Text classification is the task of assigning a set of predefined categories to free-text. Text classifiers can be used to organize, structure, and categorize pretty much anything. What is it? Suppose you distribute documents in certain categories. A new document arrives, and it is necessary to determine to which category it belongs. By using NLP, text classifiers can automatically analyze text and then assign a set of pre-defined tags or categories based on its content.

9. Character Recognition

Character Recognition systems also have numerous applications like receipt character recognition, invoice character recognition, check character recognition, legal billing document character recognition, and so on.

10. Spell Checking

A spell checker is a software tool that identifies and corrects any spelling mistakes in a text. Most text editors let users check if their text contains spelling mistakes. One of the most vivid examples is the Grammarly app. It is an online grammar checker that scans your text for all types of mistakes, from typos to sentence structure problems and beyond.

Wrapping it up: What makes NLP Difficult?

The very nature of human natural language makes some NLP tasks difficult: not all laws can be effectively formalized, some phenomena are very abstract. For example, the task of automatically detecting sarcasm, irony, and implicatures in texts has not yet been effectively solved. NLP technologies still struggle with the complexities inherent in elements of speech such as similes and metaphors.

But, I think we shouldn’t wait perfect results right from the start. Today, NLP is great for solving tasks associated with morphological word processing: determining the initial form of words and all possible word forms. NLP is great for solving classification problems. The task of personal assistants, tuned to a specific area of services, is more or less well solved: book a table in a restaurant, buy a ticket for a plane and more. Let’s do not rush thongs and see what will be next.

Thanks for reading!

#MachineLearning #DataScience