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Natural Language Processing is the technology used to aid computers to understand the human’s natural language. It’s not an easy task teaching machines to understand how we communicate.

Contents

1 What is NLP?
- 1.1 Examples of NLP
2 Conclusion

What is NLP?

Natural Language Processing, usually shortened as NLP, is a branch of artificial intelligence that deals with the interaction between computers and humans using the natural language. The ultimate objective of NLP is to read, decipher, understand, and make sense of the human languages in a manner that is valuable. Most NLP techniques rely on machine learning to derive meaning from human languages. The five phases of NLP involve lexical (structure) analysis, parsing, semantic analysis, discourse integration, and pragmatic analysis. Some well-known application areas of NLP are Optical Character Recognition (OCR), Speech Recognition, Machine Translation, and Chatbots.

Natural Language Processing (NLP) and Natural Language Generation (NLG) have gained importance in the field of Machine Learning (ML) due to the critical need to understand text, with its varying structure, implied meanings, sentiments, and intent. Natural Language Processing and Natural Language Generation have removed many of the communication barriers between humans and computers by translating machine language into human language, and by creating opportunities for humans to accomplish tasks that were impossible before.

In Natural Language Processing, the Machine Learning training algorithms study millions of examples of text — words, sentences, and paragraphs — written by humans. By studying the samples, the training algorithms gain an understanding of the “context” of human speech, writing, and other modes of communication. This training helps NLP software to differentiate between meanings of various texts.

Examples of NLP

We shall consider some small and easy examples of Natural Language Processing.

A human talks to the machine
The machine captures the audio
Audio to text conversion takes place
Processing of the text’s data
Data to audio conversion takes place
The machine responds to the human by playing the audio file

What are the techniques used in NLP?

Syntactic analysis and semantic analysis are the main techniques used to complete Natural Language Processing tasks.

Here is a description on how they can be used.

Syntax

Syntax refers to the arrangement of words in a sentence such that they make grammatical sense.

In NLP, syntactic analysis is used to assess how the natural language aligns with the grammatical rules.

Computer algorithms are used to apply grammatical rules to a group of words and derive meaning from them.

Here are some syntax techniques that can be used:

Lemmatization: It entails reducing the various inflected forms of a word into a single form for easy analysis.
Morphologicalsegmentation: It involves dividing words into individual units called morphemes.
Word segmentation: It involves dividing a large piece of continuous text into distinct units.
Part-of-speech tagging: It involves identifying the part of speech for every word.
Parsing: It involves undertaking grammatical analysis for the provided sentence.
Sentence breaking: It involves placing sentence boundaries on a large piece of text.
Stemming: It involves cutting the inflected words to their root form.

2. Semantics

Semantics refers to the meaning that is conveyed by a text. Semantic analysis is one of the difficult aspects of Natural Language Processing that has not been fully resolved yet.

It involves applying computer algorithms to understand the meaning and interpretation of words and how sentences are structured.

Here are some techniques in semantic analysis:

Named entity recognition (NER):It involves determining the parts of a text that can be identified and categorized into preset groups. Examples of such groups include names of people and names of places.
Word sense disambiguation:It involves giving meaning to a word based on the context.
Natural language generation: It involves using databases to derive semantic intentions and convert them into human language.

Natural Language Processing and Future

Advancements in NLP have implications in Data Governance. NLP gathers copious amounts of data from users, raising important legal issues about data ownership, privacy, and security. NLP will take over more control of what we see and do, but big brother isn’t the government, it’s the big tech corporations: Google, Microsoft, Facebook, Amazon, and others. Governments, to be effective, will need to develop new regulations around how data gathered and disseminated through NLP. Especially where NLP will be tied to financial gain. NLP technology will continue to gain momentum. If you get into a car accident in China in the near future, you’ll be able to pull out your smartphone, take a photo, and file an insurance claim with an AI system.

Uses of NLP

Although there are many uses of Natural Language Processing, we shall consider a few and important ones here that lead to a change in technological world. Today’s ever changing world needs everyone to cope up with it. We need get to know all the new emerging technologies and hence, Natural Language Processing is one of it. Natural Language Processing has a lot of uses and applications and are ever increasing with new technologies coming in picture. Natural Language Processing is one of the basic and major applications in Artificial Intelligence and Machine Learning.

Using NLP to Communicate and Summarize Complex Big Data

Business managers have a Big Data problem. They puzzle over dashboards and spreadsheets drowning in too much data and trying to compile it all together into meaningful information. It literally takes an organization’s data and transforms it into language, not standard computer-generated text that is overly technical and difficult to read, but natural human language that reads like a literate and well-educated person wrote it.

Using NLP to Turn Language into Useful Data

Question answering technology built on 200 million text pages, encyclopedias, dictionaries, thesauri, taxonomies, ontologies, and other databases has gained traction. Natural Language Processing has much promise in Data Security as well.

Conclusion

Natural Language Processing plays a critical role in supporting machine-human interactions. As more research is being carried in this field, we expect to see more breakthroughs that will make machines smarter at recognizing and understanding the human language.