Natural Language Processing is the program for the computer to understand human language as it is spoken. Natural Language Processing is abbreviated as NLP. NLP is an emerging technology form of Artificial Intelligence (A.I.). NLP is different from Artificial Intelligence as it does not focus on Voice, it draws on the contextual pattern. The Natural Language Processing helps machine understand and even communicate with human speech.

NLP is an advanced version of Artificial Intelligence. The first step of Natural Language Processing depends on the application of the system. The A.I. based Alexa or Google Assistance translates speech into text. This process is done by using the ‘Hidden Markov Models System’ (HMS). Each NLP System follows slightly different techniques, whereas, overall it looks almost similar. NLP is a pursuit which fills the gap between Human Communication and Computer Understanding. Natural Language Processing makes possible for the computer to read the text, hear the speech, interpret it, measure requirements, and analyze which part is important first.

NLP can do Sentiment Analysis too. Using Sentiment Analysis, data scientists can assess comments from Social Media and analyze how their business or any particular brand is performing. NLP Deep Learning Model algorithm allows to read text on Web Pages, analyzes its meaning and translates into other required languages.

How Natural Language Processing Works?

Natural Language Processing includes many different techniques to interpret Human Language. Basic NLP task requires tokenization and parsing, part-of-speech tagging, language detection and identification of semantic relationship. Normally, NLP breaks down language into short and informative pieces. Then, try to analyze the relationship between the pieces and explore how the pieces work together and form a meaning.

The basic approach of Natural Language Processing is based on Deep Learning. Deep Learning is a type of Artificial Intelligence which analyzes and uses a pattern over data to enhance the program’s understanding. Deep Learning Model requires a huge number of labeled data to identify the relevant relation and assemble these data into one main hurdle to NLP.

The earlier method for NLP involves a more rule-based approach. Whereas, the Simpler Machine Learning Algorithm explains what word or phrases to look for text and give specific responses respective to the appeared words or phrases. In spite of that Deep Learning is more flexible and follows an intuitive approach in which the algorithm tries to identify the speaker.

The tasks often used in Higher-Level NLP capabilities, like:

1. Categorization of Content: The language based document summary, including search and indexing, detect duplicate content and generate the content alert.

2. Extraction of Context: Automatically fetches structured information from the source of text.

3. Document Summarization: Generate summary automatically of the larger text.

4. Machine Translation: Translate Text or Speech automatically from one language to another.

5. Conversion of Speech-to-Text and Vice-Versa: Automatically fetch structured information from text-based sources.

Advantages of Natural Language Processing:

The specific requirements and unique structured voice commands make it tough to develop NLP Application. Therefore, the development stage is quite complex, using NLP in the applications has a lot of advantages:

1. Automatic Summarization: It produces a short and readable summary of a part of the text.

2. Co-reference Resolution: It finds which word refer to the same objects, from the given sentence or the paragraph.

3. Discourse Analysis: It includes various related tasks which could relate to the discourse structure of the connected text.

4. Always the better result: It is different from keyword search or text-oriented search. Meaning-to-meaning search provides the result which is correct as per the text inputted. Whereas, NLP delivers the search result by understanding the intention of the customer, as soon as the customer hits search.

5. The Intention of Customer is translated through Search Process: The customer who is searching is a human, and human does mistakes of spelling or confused with the product name or brand. So, this error gap can only be filled with how robust your on-site search is. NLP still tries to fetch the information and tries to keep the search unified, even in the case of typos or abrupt information.

6. More Data Extraction means more data growth: Identifying about what customers want to search or is searching for, became a key in improving the business. Therefore, with NLP you can learn about customer requirements, preference, needs, and habits. This data inference can be used in various situations like marketing, SEO, Campaigning, Promotion, and lot more.

7. Results get affected due to Complex Search: Handling a number of variables in a single search means providing a collective result which might be the end of customers need. Language processing looks with a broader picture, not just on the searched keywords, and provides the result as a compiled form with it. The result may be wrongly identified with the text-based search or might be possible that it missed out with the keyword queries.