NLP is Natural Language Processing. It is dependent on Computer Science, Artificial Intelligence, and Human Language. NLP is the technology that is used by machines for understanding, analyzing, manipulating, and interpreting human languages. Developers highly use it in completing tasks like speech recognition, translation, automatic summarization, Named Entity Recognition (NER), relationship extraction, and topic segmentation.

The two main components of NLP are:

• Natural Language Understanding (NLU)

NLU extracts the metadata from contents like keywords, concepts, entities, emotions, relations, and semantic roles, through which it helps the machines to understand and analyze the human language.

NLU is mainly used in business applications for understanding customer needs both in written and spoken language. NLP is used in mapping the input to the proper representation. It is also used in analyzing the various aspects of language. 

• Natural Language Generation (NLG)

NLG helps in converting the computerized data into natural language representation. It acts as a translator. It mainly covers text planning, sentence planning, and text realization.

NLU is more complicated than NLG. Producing non-linguistic outputs from natural language inputs is done by NLU. In contrast, NLG obtains constructing natural language outputs from non-linguistic inputs.

Applications of NLP are:

• Question Answering: NLP helps in developing systems that can automatically answer your questions when asked in a natural language. For example, Alexa.
• Spam Detection: You can train your model with the help of NLP regarding the separation of wanted and unwanted emails. This allows spam detection and getting rid of unwanted emails from user inboxes.
• Sentiment Analysis: It is used on the web to detect and analyze the user’s behavior, attitude, and emotional state. A combination of NLP and statistics is used to develop this application that assigns values to the text in order to identify the mood of the context. It is also known as Opinion Mining.
• Machine Translation: Machine translation is usually used for translating a text or a speech of one natural language to another, for example, Google Translator.
• Spelling Correction: Many software uses auto-correction for correcting typed sentences like MS Word, MS Powerpoint, Google Docs, etc. This is achieved through NLP.
• Speech Recognition: Speech recognition is the conversion of spoken words into text. This can be implemented using NLP. It is vastly used in applications like dictating to MS Word, mobiles, voice user interface, home automation, and more.
• Chatbot: NLP’s most important application is the implementation of chatbot. Nowadays, a chatbot is a necessary tool on every website that intends to know their customer better. Most companies have adopted this method for better growth.
• Information Extraction: NLP is used for extracting structured data from semi-structured or unstructured machine-readable files. It is considered one of the critical applications of NLP.
• Natural Language Understanding (NLU): NLU converts a large group of text to first-order logic structures, which is one of the formal representations that are easier for computers to understand and manipulate the notations of the natural language.

To build an NLP pipeline, you need to follow the following steps:

• Sentence Segmentation
• Word Tokenization
• Stemming
• Lemmatization
• Identifying Stop Words
• Dependency Parsing
• POS Tags
• Named Entity Recognition (NER)
• Chunking
   

There are five phases of NLP, namely:
 

• Lexical Analysis
• Syntactic Analysis
• Semantic Analysis
• Discourse Integration
• Pragmatic Analysis

Advantages of NLP include:
 

• NLP helps users to get direct responses just by asking questions regarding any subject.
• NLP provides appropriate answers to the questions asked. It avoids giving unnecessary information.
• It helps machines to communicate with humans in their natural language.
• It is very time efficient.
• NLP is adopted by many companies, which helps them improve their efficiency of the documentation process, the accuracy of documentation, and the identification of the information from large datasets.

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