What is Natural Language Processing(NLP)? | Importance of Natural Language Processing

What is Natural Language Processing?

Natural language processing or NLP is an automated way to understand analyze human languages and extract information from such data by applying machine learning algorithms the data content can be a text document, image, audio, or video.                                                                                                 

Sometimes it's also referred to as a field of computer science or Artificial Intelligence(Ai) to extract the linguistics information from the underlying data.

NLP enables machines or computers to derive meaning from human or natural language input.

Why Natural Language Processing? 

The world is now connected globally due to the advancement of technology and devices and this has resulted in the high volume of digital data across the world, and has led to several challenges in analyzing data including -

1. Analyzing the tons of Data

2. Identifying Various Languages and dialects 

3. Applying quantitative analysis

3. Handling Ambiguities 

This is where natural language processing proves useful.

One of the main goals of natural language processing is to understand various languages process them and extract information from them, in NLP full automation can be easily achieved by modern software libraries, modules, and packages.

These software libraries and packages are aware of diverse language and culture and categorized data accordingly which enables understanding cinematic better with the help of these libraries we can also build analytical models and automate the natural language process with minimum or no human interventions.

NLP Technology

NLP terminology now that you have understood why NLP is so important in recent times, it's time to make yourself comfortable with the NLP terminologies.

1. Word boundaries - It determines where one word ends and the other begins.

2. Tokenization - It's a technique to split words phrases idioms etc present in a document.

3. Temming - It's a process to map words to their stem or root, it's very useful in finding synonyms and extensively used in search engines.

4. Tf-Idf - Its numerical value which represents how important a word is to a document or corpus.

5. Semantic analytics - It's a technique in vectorial semantics of analyzing relationships between a set of documents in the terms it contains by producing a set of concepts related to the documents and tons.

6. Sambiguation - It's a technique to determine the meaning and a sense of words context versus intent.

7. Topic Models - It's a type of statistical model for finding abstract topics which occur in a collection of documents.