Importance of NLP in Data Science

Key Concepts | Working & Advantages | Scope & Career Growth

Mohsin Raza

Published in

TheLeanProgrammer

6 min readJun 12, 2021

Introduction to NLP

NLP stands for Natural Language Processing. It is a subfield or branch of Artificial intelligence (AI) that enables computers to understand human languages and process them in a manner that is valuable. It concerns the interactions between human spoken (natural) languages like English and computers.

What is Natural Language Processing(NLP)?

Natural Language Processing, or NLP for short, is broadly defined as the automatic manipulation of natural languages, like speech and text, by software that helps computers to observe, analyze, understand, and derive valuable meaning from natural or human spoken languages.

Tasks like translation, automatic summarization, and relationship extraction, speech recognition, named entity recognition, topic segmentation, and sentiment analysis can be performed by developers using Natural language processing (NLP).

NLP applications are hard or challenging as computers require humans to interact with them using programming languages like Java, Python, etc., which are structured and unambiguous. But human spoken languages are ambiguous and change with regional or social change; hence it becomes difficult to train computers to understand natural languages.

Components of NLP

Basically, there are two components of the Natural Language Processing systems:

A. Natural Language Understanding (NLU)

In this, we have to understand the basic tasks −Basically, mapping a given input in natural language to useful representations.

Difficulties in NLU:

NL has an extremely rich form and structure.

It is very ambiguous. There can be different levels of ambiguity −
Lexical ambiguity − It is a very primitive level such as word-level.
For example, treating the word “board” as a noun or verb?
Syntax Level ambiguity − A sentence can be parsed in different ways.
For example, “He lifted the beetle with the red cap.” − Did he use the cap to lift the beetle or he lifted a beetle that had a red cap?
Referential ambiguity − Referring to something using pronouns. For example, Rima went to Gauri. She said, “I am tired.” − Exactly who is tired?
One input can mean different meanings.
Many inputs can mean the same thing.

Applications of NLU

Sentiment Analysis
Profanity Filtering
Spam Filtering
Speech Recognition
Chatbots
Text-Summarization

B. Natural Language Generation (NLG)

We have to produce meaningful phrases and sentences. That is in the form of natural language from the internal representation.
As this process involves:

Text planning − It includes retrieving the relevant content from the knowledge base.

Sentence planning − It includes choosing required words, forming meaningful phrases, setting the tone of the sentence.

Text Realization − It is mapping sentence plan into sentence structure.

Applications of NLG:

Chatbots
Text Summarization
Voice Assistants
Image Captioning

Steps in NLP

There are generally five steps:

Lexical Analysis − It involves identifying and analyzing the structure of words. Lexicon of a language means the collection of words and phrases in a language. Lexical analysis is dividing the whole chunk of text into paragraphs, sentences, and words.
Syntactic Analysis (Parsing) − It involves analysis of words in the sentence for grammar and arranging words in a manner that shows the relationship among the words. A sentence such as “The school goes to the boy” is rejected by an English syntactic analyzer.
Semantic Analysis − It draws the exact meaning or the dictionary meaning from the text. The text is checked for meaningfulness. It is done by mapping syntactic structures and objects in the task domain. The semantic analyzer disregards sentences such as “hot ice cream”.
Discourse Integration − The meaning of any sentence depends upon the meaning of the sentence just before it. In addition, it also brings about the meaning of an immediately succeeding sentence.
Pragmatic Analysis − During this, what was said is re-interpreted on what it actually meant. It involves deriving those aspects of language which require real-world knowledge.

Advantages of NLP

Automatic summarization
Coreference resolution
Discourse Analysis
Relieves the burden of syntax learning
No training required
Allows non-programmers to interact with computing systems
Common synonyms of input can be interpreted
Better and efficient results
Search processing tells what user exactly mean
More data mining means more data for growth
Can perform complex search
Contextual understanding

Required Skills

Programming skills
Knowledge about machine learning
Common NLP problems
NLP tools
Ability to understand research papers
Building prototypes
Capability to learn new stuff
Software engineering skills
Probability and statistics
Linguistic knowledge
Recursive neural networking

Why NLP is important?

With the increasing amount of text data being generated every day, NLP will only become more and more important to make sense of the data and used it in many other applications. Chances are you’ve already been using some of the most powerful NLP applications but not yet aware of that.
Just take a look at Google Translate. It is a free multilingual machine translation service developed by Google, powered by NLP behind the scene.
Or maybe you’ve tried Amazon Alexa or Google Assistant to understand and answer your questions accurately via speech recognition — again, through NLP.

NLP has changed the way we interact with computers and it’ll continue doing so in the future. These AI technologies will be the underlying force for transformation from data-driven to intelligence-driven endeavors, as they shape and improve communication technology in the years to come.

How will this technology help you in career growth?

The growing applications and interest in the field of natural language processing have created various opportunities for professionals specializing in Machine Learning, Computational Linguistics, and Data Science. Big companies like Google, Facebook, Forte Group, Sony Ericsson, Merrill Lynch, British Airways, Sainsbury, J.P. Morgan, Celtic, American Express and Ernst & Young, and many more hire natural language processing analysts and experts.

Various job roles related to Natural language processing are NLP scientists, NLP engineers, NLP architects, NLP applied research scientists, cognitive data scientists, Voiceover artists, and many more. The average salary for jobs related to natural language processing ranges from $76,343 per year to $142,912 per year.

Conclusion

If you are interested in interacting with computing systems and have programming and linguistic knowledge, learning, natural language processing is valuable. Due to an increase in data and the need to interact with computers, the need for natural language processing is increasing day by day, and various job opportunities are coming into the market with handsome packages. Therefore there is a great scope of NLP in the future.