Applications of NLP

How NLP is used

NLP is already an integral part of our daily life. In modern life, a larger and larger portion of our daily communication is done online, and our online communication is still largely conducted in natural language text. Think of your favorite social networking services, such as Facebook and Twitter. Although you can post photos and videos, a large portion of communication is still in text. As long as you are dealing with text, there is a need for NLP.

For example, how do you know if a particular post is spam? How do you know which posts are the ones you are most likely to “like?” How do you know which ads you are most likely to click?

Because many large internet companies need to deal with text in one way or another, chances are many of them are already using NLP. You can also confirm this from their “careers” page—you’ll see that they are always hiring NLP engineers and data scientists. NLP is also used to a varying extent in many other industries and products including, but not limited to, customer service, e-commerce, education, entertainment, finance, and health care, which all involve text in some ways.

Many NLP systems and services can be classified into or built by combining some major types of NLP applications and tasks. In this section, I’m going to introduce some of the most popular applications of NLP as well as common NLP tasks.

NLP applications

An NLP application is a software application whose main purpose is to process natural language text and draw some useful information from it. Similar to general software applications, it can be implemented in various ways, such as an offline data-processing script, an offline standalone application, a backend service, or a full-stack service with a frontend, depending on its scope and use cases.

MACHINE TRANSLATION

Machine translation is probably one of the most popular and easy-to-understand NLP applications. Machine translation (MT) systems translate a given text from one language to another language. An MT system can be implemented as a full-stack service (e.g., Google Translate), as well as a pure backend service (e.g., NLP SaaS products). The language the input text is written in is called the source language, whereas the one for the output is called the target language.

MT encompasses a wide range of NLP problems, including language understanding and generation, because MT systems need to understand the input and then generate the output. MT is one of the most wellstudied areas in NLP, and it was one of the earliest applications of NLP as well.

GRAMMATICAL AND SPELLING ERROR CORRECTION

Most major web browsers nowadays support spelling correction. Even if you forget how to spell “Mississippi,” you can do your best and type what you remember, and the browser highlights it with a correction. Some word-processing software applications, including recent versions of Microsoft Word, do more than just correct spelling. They point out grammatical errors such as uses of “it’s” instead of “its.” This is not an easy feat, because both words are, in a sense, “correct” (no mistakes in spelling), and the system needs to infer whether they are used correctly from the context.

Some commercial products (most notably, Grammarly, https://www.grammarly.com/) specialize in grammatical error correction. Some products go a long way and point out incorrect usage of punctuation and even writing styles. These products are popular among both native and non-native speakers of the language.

SEARCH ENGINE

Another application of NLP that is already an integral part of our daily lives is search engines. Few people would think of search engines as an NLP application, yet NLP plays such an important role in making search engines useful that they are worth mentioning here. Page analysis is one area where NLP is heavily used for search engines. Ever wonder why you don’t see any “hot dog” pages when you search for “dogs?” If you have any experience building your own full-text search engines using open source software such as Solr and Elasticsearch, and if you simply used a word-based index, your search result pages would be littered with “hot dogs,” even when you want just “dogs.” Major commercial search engines solve this problem by running the page content being indexed through NLP pipelines that recognize that “hot dogs” are not a type of “dogs.” But the extent and types of NLP pipelines that go into page analysis is confidential information for search engines and is difficult to know.

Query analysis is another NLP application in search engines. If you have noticed Google showing a box with pictures and bios when you search for a celebrity or a box with the latest news stories when you search for certain current events, that’s query analysis in action. Query analysis identifies the intent (what the user wants) of the query and shows relevant information accordingly. A common way to implement query analysis is to make it a classification problem, where an NLP pipeline classifies queries into classes of intent (e.g., celebrity, news, weather, videos), although again, the details of how commercial search engines run query analysis are usually highly confidential.

DIALOG SYSTEMS

Dialog systems are machines that humans can have conversations with. The field of dialog systems has a long history. One of the earliest dialog systems, ELIZA, was developed in 1966.

The two main types of dialog systems are task-oriented and chatbots. Task-oriented dialog systems are used to achieve specific goals (for example, reserving a plane ticket), obtaining some information, and, as we saw, making a reservation at a restaurant. Task-oriented dialog systems are usually built as an NLP pipeline consisting of several components, including speech recognition, language understanding, dialog management, response generation, and speech synthesis, which are usually trained separately.

The other type of dialog system is chatbots, whose main purpose is to have conversations with humans. Traditional chatbots are usually managed by a set of handwritten rules (e.g., when the human says this, say that). Recently, the use of deep neural networks, particularly sequence-to-sequence models and reinforcement learning, has become increasingly popular. However, because the chatbots do not serve particular purposes, the evaluation of chatbots, that is, assessing how good a particular chatbot is, remains an open question.

Natural Language Processing(NLP) tasks

Behind the scenes, many NLP applications are built by combining multiple NLP components that solve different NLP problems. In this section, I introduce some notable NLP tasks that are commonly used in NLP applications.

TEXT CLASSIFICATION

Text classification is the process of classifying pieces of text into different categories. This NLP task is one of the simplest yet most widely used. You might not have heard of the term “text classification” before, but I bet most of you benefit from this NLP task every day. For example, spam filtering is one type of text classification. It classifies emails (or other types of text, such as web pages) into two categories—spam or not spam. This is why you get very few spam emails when you use Gmail and you see so few spammy (low-quality) web pages when you use Google.

PART-OF-SPEECH TAGGING

A part of speech (POS) is a category of words that share the similar grammatical properties. In English, for example, nouns describe the names of things like objects, animals, people, and concepts, among many other things. A noun can be used as a subject of a verb, an object of a verb, and an object of a preposition.

Verbs, in contrast, describe actions, states, and occurrences. Other English parts of speech include adjectives (green, furious), adverbs (cheerfully, almost), determiners (a, the, this, that), prepositions (in, from, with), conjunctions (and, yet, because), and many others. Almost all languages have nouns and verbs, but other parts of speech differ from language to language. For example, many languages, such as Hungarian, Turkish, and Japanese, have postpositions instead of prepositions, which are placed after words to add some extra meaning to them. A group of NLP researchers came up with a set of tags that cover frequent parts of speech that exist in most languages, called a universal part-of-speech tagset