What is natural language processing?

Natural language processing (NLP) is a computer program's capacity to comprehend natural language, or human language as it is spoken and written, is known as natural language processing (NLP). It is a part of machine intelligence (AI).


NLP has been around for more than 50 years and has linguistic origins. It has several practical uses in a range of industries, including corporate intelligence, search engines, and medical research.


How does natural language processing work?

Computers can now comprehend natural language just like people do thanks to NLP. Natural language processing use artificial intelligence to take real-world information, process it, and make sense of it in a way that a computer can comprehend, regardless of whether the language is spoken or written. Computers have reading programmes and microphones to gather audio, much as people have various sensors like ears to hear and eyes to see. Computers have a programme to process their various inputs, just as humans have a brain to do so. The input is eventually translated into computer-readable code during processing.


The creation of algorithms and data preparation are the two fundamental stages of natural language processing.


Preparing and "cleaning" text data so that computers can examine it is known as data preparation. Preprocessing prepares data for use and emphasises text characteristics that an algorithm can use. This can be accomplished in a number of ways, including:

  • Tokenization. Text is divided into manageable chunks at this point.


  • halt word deletion Common words are eliminated in this case, leaving just the special words that reveal the most about the text.


  • stemming and lemmatization. Words are boiled down to their basic components at this point for processing.


  • speech-act tagging. Words are then labeled according to the part of speech they belong to, such as nouns, verbs, and adjectives.


  • system based on rules. The language rules of this system were thoughtfully created. The usage of this strategy dates back to the early stages of the development of natural language processing.


  • system based on machine learning. Statistical techniques are used in machine learning algorithms. They are fed training data to help them learn how to execute tasks, and when additional training data is processed, they modify their techniques. The algorithms used in natural language processing refine their own rules through repeated processing and learning using a combination of machine learning, deep learning, and neural networks.


Why is natural language processing important?


Businesses need a means to effectively process the vast amounts of unstructured, text-heavy data they utilize. Up until recently, companies were unable to efficiently evaluate the natural human language that makes up a large portion of the information produced online and kept in databases. Natural language processing comes in handy in this situation.


Consider the following two sentences: "Cloud computing insurance should be part of every service-level agreement" and "A solid SLA provides an easier night's sleep — even in the cloud" to demonstrate the benefit of natural language processing. If a user uses natural language processing to do a search, the software will understand that cloud computing is a thing, that cloud is a shorthand for cloud computing, and that SLA is an abbreviation for service-level agreement, used in the business world.


These are the kinds of ambiguous expressions that are regularly seen in spoken language and that machine learning algorithms have historically struggled to understand. Algorithms are now capable of successfully interpreting them because of advancements in deep learning and machine learning techniques. These enhancements increase the volume and quality of the data that can be studied.


Techniques and methods of natural language processing


This is a sentence's grammatical analysis. An example: the phrase "The dog barked" is supplied to a natural language processing algorithm. Parsing is dividing this statement into its component pieces, such as dogs as a noun and barking as a verb. This is helpful for more difficult downstream processing jobs.


Word segmentation

This is the process of extracting word formations from a string of text. An individual scans a handwritten note into a computer, for instance. The algorithm would be able to examine the page and identify that white spaces separate the text.

Sentence breaking

In long texts, this establishes sentence boundaries. Example: Text is input into a natural language processing system, "The canine yipped." I awoke. " The sentence breaking used by the algorithm to break up the sentences is the period.


Morphological segmentation

As a result, words are split up into units known as morphemes. As an illustration, the algorithm would convert the word untestably into [[un[[test]able]]ly, where "un," "test," "able," and "ly" are all recognized as morphemes. Speech recognition and machine translation both benefit greatly from this.


In this way, words with inflection are separated into their base forms. The algorithm would be able to identify the word "barked" as having the root "bark" in the phrase "The dog barked." This would be helpful if a user were searching a text for every occurrence of the term "bark" and all of its verb forms. Even when the characters are different, the computer can still tell that they are fundamentally the same word.


Semantics deals with the use of language and its underlying meaning. Algorithms are used in natural language processing to comprehend sentence structure and meaning. Semantics methods consist of:

Word sense disambiguation

This uses context to determine a word's meaning. Example: Think about the phrase "The pig is in the pen." There are several meanings for the word pen. This approach enables an algorithm to recognise that the term "pen" in this context refers to a fenced-in space rather than a writing tool.

Named entity recognition

This establishes which words may be divided into groups. Using this technique, an algorithm may examine a news story and find any references of a certain business or item. It would be able to distinguish between items that seem the same using the semantics of the text. For instance, the algorithm might identify the two occurrences of "McDonald's" as two distinct entities—one a business and the other a person—in the phrase "Daniel McDonald's son went to McDonald's and got a Happy Meal."

Natural language generation

To identify the meaning of words and create new text, this requires a database. Example: By associating specific terms and phrases with aspects of the data in the BI platform, an algorithm may automatically produce a summary of findings from the BI platform. Another illustration would be the automatic creation of news stories or tweets based on a certain body of training content.