NATURAL LANGUAGE PROCESSING (NLP): Definition and Technique Types

The different definitions that are present on the websites always says that Artificial Intelligence (AI) is about mirroring the intelligence of a human mind. Among these intelligence, Language is a vital efficiency of human beings to interact with each other. 

This is the reason why big corporate companies are working to make Artificial Intelligence (AI) in the form of a language called NATURAL LANGUAGE PROCESSING or NLP. Natural Language Processing (NLP) is a junction of Artificial Intelligence, Linguistics and Computer Science. 

The main aim of NLP is for computers to recognize the human’s natural language to complete all our digital textual tasks.

Natural Language Processing (NLP) has become a most important and necessary part of Artificial Intelligence (AI). Some of the Natural Language Processing (NLP) applications are voice interfaces, sentiment analysis, dialog systems, complex question answering, chatbots, keyword search, information extraction, machine translation, digital virtual assistants and much more.

Additionally many more applications of Natural Language Processing (NLP) are developed everyday.

Natural Language Processing(NLP) Definition 

Natural Language processing (NLP) is a branch of Artificial Intelligence (AI) that helps computers to learn, illustrate and employ human language. NLP brings from many developments including computational linguistics and computer science, in its inquiry to fill the hole between computer understanding and human communication. 

Natural Language Processing (NLP) is the computerized manipulation of natural languages such as text or speech by software. The Natural Language Processing (NLP) study has grown out with the rise of computers in the field of linguistics. 

Natural Language Processing (NLP) uses the meaning and structure of human speech to analyze various forms such as morphology, syntaxes, pragmatics and semantics. To solve problems and to perform tasks, computer science converts language knowledge into rules based on machine learning algorithms.  

This is how email tabs got segregated by Gmail, textual grammar connection by software, understanding capacity of voice assistants and filtering and content categorization by systems. 

Some of the Natural Language Processing (NLP) techniques are

1) Named Entity Recognition (NER)

2) Tokenization

3) Sentiment Analysis

4) Automatic Text Summarization

5) Keyword Detection for SEO Techniques

6) Topic Modelling

1) Named Entity Recognition (NER)

Named Entity Recognition (NER) is used to extract information for segregating “name entities” into pre-defined groups. These groups contain details of persons name to locations, organizations, percentages, time expressions, monetary values, etc. 

The ‘what’ and ‘why’ type of world problems can be answered by Named Entity Recognition (NER) in Natural Language Processing (NLP). With Named Entity Recognition (NER) the name of person or business mentioned in the article, product details and information, locations of useful entities can be identified easily. 

Searching for new articles automatically and extracting information from the post, such as people, person, organization, business, locations, celebrities can be done by Entity Categorization algorithm. This algorithm also sorts news content easily into different groups. 

2) Tokenization

The most common actions in dealing with text information is Tokenization. It divides the given text into a list of tokens that contains characters, numbers, punctuations, phrases, sentences and more. 

The primitive stage of any Natural Language Processing (NLP) problem is mapping sentences from characters to strings and strings to words. 

Tokenization is the crucial part of every Information Retrieval (IR) framework. This IR not only encompasses the pre-processing of text but also generates tokens that are used in the ranking/indexing process. 

There are many techniques available in tokenization, in that Porter Algorithm is one of the most popular techniques. 

3) Sentiment Analysis

Sentiment Analysis updates us in case our data is interacted with an optimistic and pessimistic outlook. To produce sentiment analysis there are various techniques to define the empathy conveyed in a statement or assemblage of sentences in order to accomplish a general perception of the customer’s mood. 

This can be very important in understanding how customers respond to types of communication in the field of Marketing. 

Sentiment Analysis uses both supervised and unsupervised methods. Supervised models known as Naive Bayes and other Machine Learning methods like gradient boosting or random forests are used for sentiment analysis. And it also uses an unsupervised approach known as lexicon-based strategies. 

4) Automatic Text Summarization

This method is to produce a pithy and correct text summary from various text tools that can range from blog posts, emails, academic posts, tweets, new stories and books. Because of the availability of immense volumes of textual data, the demand for Automated Text Summarization is increasing nowadays.

This Text summarization can be splitted into two categories: 

Extractive summarization – It seperates phrases and sentences from a text and     heaping them together to create a description. 

Abstractive summarization – It uses practical Natural Language Processing (NLP) techniques to produce a new brand description. 

5) Keyword Detection for SEO Techniques

This is a technique which creates a list of common words in your text data and compares it to the search engine optimization keyword list.

 Business people can easily scan the data to find distinctive substantive and relevant keywords. 

6) Topic Modelling

Topic modelling is a technique to break a big text or content into related ideas and keywords. It is a technique of unsupervised machine learning algorithms. 

Latent Dirichlet Algorithm investigates the text body, divides phrases and words, and then obtains different topics. 

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