NLP- Natural Language Processing

NLP- Natural Language Processing

In this article today we are going to study Natural Language Processing. So NLP stands for Natural Language Processing, It is a part of Computer Science, Human language, and Artificial Intelligence. NLP is the technology that is used by machines to understand, analyze, manipulate, and interpret human languages. It helps developers to organize knowledge for performing tasks such as translation, automatic summarization, Named Entity Recognition (NER), speech recognition, relationship extraction, and topic segmentation.

Now we are going to discuss the history of NLP.

(1940-1960) - Focused on Machine Translation (MT)

Natural Languages Processing started in the year 1940s.

1948 - In the Year 1948, the first recognizable NLP application was introduced at Birkbeck College, London.

The 1950s - In the Year 1950s, Chomsky developed his first book syntactic structures and claimed that language is generative.

In 1957, Chomsky also introduced the idea of Generative Grammar, which is rule-based description of syntactic structures.

(1960-1980) - Flavored with Artificial Intelligence (AI)

In the year 1960 to 1980, the key developments were:

  • Augmented Transition Networks (ATN)

Augmented Transition Networks is a finite-state machine that is capable of recognizing regular languages.

  • Case Grammar

Case Grammar was developed by Linguist Charles J. Fillmore in the year 1968. Case Grammar uses languages such as English to express the relationship between nouns and verbs by using the preposition.

In Case Grammar, case roles can be defined to link certain kinds of verbs and objects.

For example: "Sneha broke the mirror with the hammer". In this example, case grammar identifies Sneha as an agent, mirror as a theme, and the hammer as an instrument.

In the year 1960 to 1980, key systems were:


SHRDLU is a program written by Terry Winograd in 1968-70. It helps users to communicate with the computer and move objects. It can handle instructions such as "pick up the red ball" and also answer the questions like "What is inside the white box." The main importance of SHRDLU is that it shows those syntax, semantics, and reasoning about the world that can be combined to produce a system that understands a natural language.


LUNAR is the classic example of a Natural Language database interface system that is used ATNs and Woods' Procedural Semantics. It was capable of translating elaborate natural language expressions into database queries and handling 78% of requests without errors.

1980 – current

After 1980, NLP introduced machine learning algorithms for language processing.

At the beginning of the year 1990s, NLP started growing faster and achieved good process accuracy, especially in English Grammar. In 1990 also, an electronic text was introduced, which provided a good resource for training and examining natural language programs. Other factors may include the availability of computers with fast CPUs and more memory. The major factor behind the advancement of natural language processing was the Internet.

Now, modern NLP consists of various applications, like speech recognition, machine translation, and machine text reading. When we combine all these applications then it allows artificial intelligence to gain knowledge of the world.

The following are the advantages of NLP:

  • NLP helps users to ask questions about any subject and get a direct response within seconds.

  • NLP offers exact answers to the question means it does not offer unnecessary and unwanted information.

  • NLP helps computers to communicate with humans in their languages.

  • It is very time efficient.

  • Most companies use NLP to improve the efficiency of documentation processes, the accuracy of documentation, and identify the information from large databases.

The following are the disadvantages of NLP:

  • NLP may not show context.

  • NLP is unpredictable

  • NLP may require more keystrokes.

  • NLP is unable to adapt to the new domain, and it has a limited function that's why NLP is built for a single and specific task only.

Now we will discuss its components which are Natural Language Understanding (NLU) and Natural Language Generation (NLG)

1. Natural Language Understanding (NLU)

Natural Language Understanding (NLU) helps the machine to understand and analyze human language by extracting the metadata from content such as concepts, entities, keywords, emotions, relations, and semantic roles.

NLU is mainly used in Business applications to understand the customer's problem in both spoken and written language.

NLU involves the following tasks -

  • It is used to map the given input into useful representation.

  • It is used to analyze different aspects of the language.

2. Natural Language Generation (NLG)

Natural Language Generation (NLG) acts as a translator that converts the computerized data into natural language representation. It mainly involves Text planning, Sentence planning, and Text Realization.

Now we go to its applications, following are some applications of NLP -

1. Question Answering

Question Answering focuses on building systems that automatically answer the questions asked by humans in a natural language.

2. Spam Detection

Spam detection is used to detect unwanted e-mails getting to a user's inbox. Also used in true caller.

3. Sentiment Analysis

Sentiment Analysis is also known as opinion mining. It is used on the web to analyze the attitude, behavior, and emotional state of the sender. This application is implemented through a combination of NLP (Natural Language Processing) and statistics by assigning the values to the text (positive, negative, or natural), identifying the mood of the context (happy, sad, angry, etc.)

4. Machine Translation

Machine translation is used to translate text or speech from one natural language to another natural language.

Google Translator is an example of machine translation.

5. Spelling correction

Microsoft Corporation provides word processor software like MS-word, and PowerPoint for spelling correction.

6. Speech Recognition

Speech recognition is used for converting spoken words into text. It is used in applications, such as mobile, home automation, video recovery, dictating to Microsoft Word, voice biometrics, voice user interface, and so on.

7. Chatbot

Implementing the Chatbot is one of the important applications of NLP. It is used by many companies to provide the customer's chat services.

8. Information Extraction

Information extraction is one of the most important applications of NLP. It is used for extracting structured information from unstructured or semi-structured machine-readable documents.

9. Natural Language Understanding (NLU)

It converts a large set of text into more formal representations such as first-order logic structures that are easier for computer programs to manipulate notations of the natural language processing.

So readers today this much only.We will meet in the next blog.

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