Introduction to Natural Language Processing (NLP)

What Is Natural Language Processing (NLP)?

Natural Language Processing (NLP) is a field of artificial intelligence (AI) that enables computers to analyze and understand human language, both written and spoken. It was formulated to build software that generates and comprehends natural languages so that a user can have natural conversations with a computer instead of through programming or artificial languages like Java or C.

Understanding Natural Language Processing (NLP)

Natural Language Processing (NLP) is one step in a larger mission for the technology sector — namely, to use artificial intelligence (AI) to simplify the way the world works. The digital world has proved to be a game-changer for a lot of companies as an increasingly technology-savvy population finds new ways of interacting online with each other and with companies.

Social media has redefined the meaning of community; cryptocurrency has changed the digital payment norm; e-commerce has created a new meaning of the word convenience, and cloud storage has introduced another level of data retention to the masses.

Through AI, fields like machine learning and deep learning are opening eyes to a world of all possibilities. Machine learning is increasingly being used in data analytics to make sense of big data. It is also used to program chatbots to simulate human conversations with customers. However, these forward applications of machine learning wouldn't be possible without the improvisation of Natural Language Processing (NLP).

Stages of Natural Language Processing (NLP)

NLP combines AI with computational linguistics and computer science to process human or natural languages and speech. The process can be broken down into three parts. The first task of NLP is to understand the natural language received by the computer. The computer uses a built-in statistical model to perform a speech recognition routine that converts the natural language to a programming language. It does this by breaking down a recent speech it hears into tiny units, and then compares these units to previous units from a previous speech.

The output or result in text format statistically determines the words and sentences that were most likely said. This first task is called the speech-to-text process.

The next task is called the part-of-speech (POS) tagging or word-category disambiguation. This process elementarily identifies words in their grammatical forms as nouns, verbs, adjectives, past tense, etc. using a set of lexicon rules coded into the computer. After these two processes, the computer probably now understands the meaning of the speech that was made.

The third step taken by an NLP is text-to-speech conversion. At this stage, the computer programming language is converted into an audible or textual format for the user. A financial news chatbot, for example, that is asked a question like “How is Google doing today?” will most likely scan online finance sites for Google stock, and may decide to select only information like price and volume as its reply.

Special Considerations

NLP attempts to make computers intelligent by making humans believe they are interacting with another human. The Turing test, proposed by Alan Turing in 1950, states that a computer can be fully intelligent if it can think and make a conversation like a human without the human knowing that they are actually conversing with a machine.

One computer in 2014 did convincingly pass the test — a chatbot with the persona of a 13-year-old boy. This is not to say that an intelligent machine is impossible to build, but it does outline the difficulties inherent in making a computer think or converse like a human. Since words can be used in different contexts, and machines don’t have the real-life experience that humans have for conveying and describing entities in words, it may take a little while longer before the world can completely do away with computer programming language.