Artificial Intelligence includes Natural Language Processing (NLP). Machines can understand and pick up on human language. Text mining, sentiment analysis, machine translation, and more are all popular uses of NLP.
NLP looks at different parts of human languages, like syntax, semantics, pragmatics, and morphology, to figure out how they are put together and what they mean.
The linguistic information is then turned into rule-based machine learning algorithms that can solve problems and do tasks. Businesses have to deal with a lot of unstructured data, mostly text, and they need a quick way to process it.
A lot of the data created online and stored in databases is written in natural language, and organizations have just recently been able to evaluate this data. This is a great place for natural language processing to work.
Natural language processing solutions can simplify even huge amounts of data because their applications allow for faster processing and the use of business models to get insights from how people use language.
NLP is often used in the background of the products and apps we use daily to help companies improve the user experience.
Even though natural language processing is still developing, it is already being used in many different ways. Most of the time, you won’t notice when natural language processing is happening.
There are five main stages or phases of Natural Language Processing, starting with simple word processing and figuring out what a complicated phrase means.
Lexical or Morphological Analysis
The first step in NLP is to do a lexical or morphological analysis. It involves noticing and analysing how words are put together. The words and phrases that make up a language are called its lexicon. A text file is broken into paragraphs, phrases, and words in lexical analysis. During this phase, the source code is read as a stream of characters and turned into words people can understand. The whole book is broken up into sentences, phrases, and words.
It means studying a text by looking at each word on its own. It looks for the smallest parts of words, called morphemes. The linguistic analysis determines how these morphemes fit together and changes the word to its root form. A lexical analyser also gives the word its likely parts of speech (POS).
Syntax Analysis or Parsing
Syntactic or Syntax analysis is a way to check grammar, put words in the right order, and show how they relate to each other. It involves looking at how the words in the phrase fit together and putting them in a way that shows how they are related. Syntax analysis makes sure that a piece of writing has the right structure. It tries to parse the sentence to ensure the sentence-level grammar is correct. A syntax analyser gives POS tags based on the structure of the sentence and the likely POS made in the previous step.
Semantic analysis is the process of figuring out what a statement means. When taken at face value, it focuses mostly on what words, phrases, and sentences mean. It also looks at how to put words together into sentences. It gets the exact meaning or dictionary definition from the text. The text is looked at to see what it means. It is done by mapping the syntactic structures and objects of the task domain.
The term “discourse integration” means understanding where something fits in. Any sentence’s meaning is based on the meaning of the sentence that comes right before it. It also sets up the meaning of the sentence that comes next. Discourse integration takes into account the sentences that come before it. That sentence or word depends on the sentence or words that came before it. The same thing goes for using proper nouns and pronouns.
The last step of NLP is pragmatic analysis, which is also the fifth step. The pragmatic analysis looks at the overall communicative and social content of a text and how it affects how it is interpreted. Pragmatic Analysis uses a set of rules that describe how people cooperatively talk to each other to help you get the desired result. It talks about how often words are used, who said what to whom, and so on. It looks at how people talk to each other, the situation in which they talk, and many other things. It means figuring out what a person or group is trying to say by how they use language. It uses what it has learned in the previous steps to translate the given text.
In this age of digital transformation and AI, where surprises are around every corner, more Natural language processing solutions will change organisations and processes even more.
Enterprises are helped to make decisions that lead to measurable results, and company efficiency is increased thanks to NLP’s actionable customer insights and the automation of several processes.