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Introduction To Natural Language Processing

Human beings are the most intelligent as well as advance species on earth and the significant reason behind this success is "Power of Communication". Now this communication occurs there in whole world in the form of languages, about 65,000 different languages exist there in world. And this leads us to develop a new field in Artificial Intelligence naming Natural Language Processing(NLP). So stay tuned to understand more about  NLP

There are huge amount of data available out there in structured as well as unstructured format. According to industrial reports, there is only approx 21% of data is structured. The structured data is in the form of speech, texts, etc,. Messages often produced on social medias like Facebook, WhatsApp is highly unstructured in nature.

 

So in order to get powerful and informative insights from this data Text Mining and NLP comes into account.

Text Mining:  Text Mining or Text Analytics is nothing but a process of deriving a not only meaningful but also powerful insights from natural language text. It usually involves the process of structuring the input text and seeking patterns from the structured text and finally interpreting output from that pattern evaluation.

NLP: Natural Language Processing:

            NLP is a one of the subfield of Computer Science and Artificial Intelligence which deals with languages used by human. Essential goal of NLP is to derive high quality information from the human language and use it for performing various operations with Data Analytics. Deep Learning, Supervised and Unsupervised learning are widely used for analyzing human languages, but Machine Learning approach for this is not sufficient and thus NLP is important because it minimizes ambiguity in language and extract powerful information.

Like that Text Mining/ Analytics and NLP go hand in hand! Both of these technologies used as a unit for many applications like Investigative discovery, Subject-matter expertise, Social media analytics and many more.

 Components Of NLP:

Natural Language of Processing have two components naming Natural Language Understanding and Natural Language Generation.

Here Natural Language Understanding deals with deriving high quality insight by mapping it from natural language and then analyze those aspects of language. Contrary to Natural language Understanding, Natural Language Generation is a process of creating variety of syntactically as well as semantically meaningful sentences and phrases in the form of natural or human language from the some internal representation.

Out of these components Natural Language Understanding takes more time to perform its task as it have to process a lot for getting those powerful insights and it becomes more critical if you are not a human. As compared to this task of Natural Language Generation is bit easy.

Steps Involved in NLP:

Tokenization: Tokenization is very first step in NLP which breaks strings into various small structures or units which are useful for creating tokens. Consider a sentence "Tokenization is first step", now this sentence is then broken into different tokens as, "Tokenization", "is", "first", "steps" and these tokens can be used for further processing.

Stemming: Stemming usually deals with normalizing words into their base or root form. For example Normalize, Normalization, Normalized, etc. words are originated from root or base Normal. In this process root word is extracted by cutting out its suffixes and prefixes.

Lemmatization:  Whereas, Lemmatization takes account of morphological analysis of the words. Process of Lemmatization collect the different inflected forms of root called Lemma and then map several words into one root.

POS Tags: POS Tags are nothing but a tags assigned to Parts of Speech in a sentence. For example, "NLP is subfield of AI", here 'NLP' and 'AI' are noun so tag is NN. It determines how a word functions in meaning as well as in sentence.

Name Entity Recognition: NER is nothing but a process of recognizing entities from a sentence like organization, quantity, person, location, etc.

Chunking: Chunking basically means picking up individual small pieces of information and grouping them together into the large pieces also known as Chunk. It helps to get meaningful insights from available information.

All these steps can be executed in Python on the platform of pythonNLTK where NLTK is Natural Language Tool Kit library.

Applications of NLP:

                One of the most important application of NLP is Sentiment Analysis of Facebook or Twitter. This application tries to analyze sentiments behind the posts or hash tags, etc.

                The next application of NLP is getting popular these days is Chatbots. These chatbots are out there in the form of costumer chat services provided by various companies.

                Furthermore we have Speech Recognition as an application of NLP. Siri, Alexa, Google Assistance are the examples of voice assistance.

                Machine Translation is also an application of NLP, one of the example of this is Google Translator. And other applications of NLP are Spell Checking, Keyword Searching, etc.

So, I hope this article have introduced you well with Natural Language Processing. You have understood the various concepts like, NLP, Text Mining, steps for performing NLP, applications of NLP. I this article must be good kick start to your knowledge of Natural Language Processing.

 


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