In the last few years, natural language processing (NLP) has evolved as a revolutionary technology in the field of data science and artificial intelligence (AI) research. The objective of NLP is processing and utilizing text and speech data to create smart machines and produce insights. It aims at building machines capable of discussing with humans about complex topics. In simple words, NLP is a combination of artificial intelligence and computational linguistics. In this post, we will look at the NLP job market, required skills, ideal learning path, and salaries, natural language processing career path, natural language processing jobs in India, nlp jobs salary etc.
Natural Language Processing (NLP) is technology used to aid computers to understand human languages. It is one of the largest branches of AI, and within it are a broad range of approaches due to the diversity in voice and text-based data.
Computers require extra support when it comes to handling unstructured datasets of human languages, which comprises different grammar languages, syntax, slangs, and dialects. NLP uses data and mathematics to help you engineer computers so that they can understand and interpret natural expressions.
Read Beginners Guide to AI, ML, Big Data, NLP, IoT, and Blockchain Technology.
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Two important functions of NLP are sentiment analysis and text categorization.
Sentiment analysis identifies the mood or subjective opinions within large amounts of text collection. It is useful for:
Text categorization is a linguistic-based document summary including search and indexing, content alerts, and duplication detection. Within text categorization, there is manual and automatic classification. For manual classification, the human annotator interprets the context of the text and categorizes it accordingly. For automatic classification machine learning (ML), NLP, and other techniques to automatically classify text in a faster and more cost-effective way.
Other approaches in NLP include topic discovery modeling, contextual extraction, speech-to-text and text-to-speech translation, and document summarization:
1. Topic discovery modeling – accurately captures the meaning and themes in text collections, and apply optimization and forecasting
2. Contextual extraction – automatically pull structured information from text-based sources.
3. Speech-to-text and text-to-speech translation – transforming voice commands into written text and vice versa
4. Document Summarization – relation Modeling, automatically generating synopses of large bodies of text. E.g. Toronto — Blue Jays, NYC — Yankees
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NLP solutions deliver immense value for organizations across different sectors, from digital communications to healthcare and medicine to finance, marketing, and retail. Here are some of the most common applications of NLP in the industry today:
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NLP engines rely on the following elements in order to process queries –
The field of NLP has become incredibly multidisciplinary, bringing together symbolic paradigms (think pattern-matching based on a set of rules) and stochastic paradigms (which draw from statistics and probability).
Here’s a look, by industry, into some ways that NLP is being used today:
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Language is highly ambiguous– it relies on subtle cues and contexts to convey meaning. Computers struggle immensely with resolving ambiguity. As a result, they fight the uphill battle of interpreting meaning without a full understanding of context, e.g. like common sense and culture.
In everyday conversation, we convey meaning without considering how our brains translate so much unstructured data into useful information. For machines, however, understanding human speech and language are very hard.
We are surrounded by text. Think about how much text you see each day:
The list is endless.
Now think about speech.
We may speak to each other, as a species, more than we write. It may even be easier to learn to speak than to write. Voice and text are how we communicate with each other.
Given the importance of this type of data, we must have methods to understand and reason about natural language, just like we do for other types of data.
“The goal of this new field is to get computers to perform useful tasks involving human language, tasks like enabling human-machine communication, improving human-human communication, or simply doing useful processing of text or speech.”
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If I say, “I love chicken”. For software or computer, it would be hard to initially understand that I mean “I love to eat chicken” and not necessarily I am into a romantic relationship with chicken.
Similarly, if someone says “I love flying planes”.
So, does that person “enjoy participating in the act of piloting an aircraft?” Or is s/he expressing “an appreciation for man-made vehicles engaged in movement through the air on wings?”
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While the rise in smart device usage, adoption of cloud-enabled solutions, and NLP-based applications to improve customer service define the NLP market growth, experts are now exploring ways to unleash its full potential in the coming years.
NLP is one of the 7 most in-demand tech skills to master in 2021. By 2025, the global NLP market is expected to reach over $34 billion, growing at a CAGR of 21.5%.
Read Careers in Computational Linguistics: Q&A with Vandana, PhD Scholar at IIT-Kharagpur & ISI-Kolkata.
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NLP aims to impart machines the ability to understand natural human languages. NLP engineers & scientists are primarily responsible for designing and developing machines and applications that can learn the patterns of speech of a human language and also translate spoken words into other languages.
The goal here is to help machines comprehend human languages as naturally as humans do. Companies typically hire NLP engineers to undertake the following tasks:
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In the US, average salary range is USD $75,000 – 110,000 per annum. In India, NLP annual salaries range from INR 4 Lacs to 9 Lacs for the folks with 1 – 4 years of experience. Below is the chart for NLP salaries in the UK and Europe.
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The primary job of NLP Scientists is to teach machines how to understand the nuances of human languages. Hence, they must be fluent in the syntax, spelling, and grammar of at least one language (the more, the better).
Also, they should have basic data science and machine learning (ML) skills. Here is the list of top technical NLP skills for the current job market.
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Here is a typical structured learning path to learn NLP over 7 months:
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Natural Language Processing Specialization
Applied Data Science with Python
Deep Learning /AI-TensorFlow Developer Professional Certificate
Logistic Regression with Python and Numpy
Advanced Statistics for Data Science
Decision Trees, SVMs, and Artificial Neural Networks
TensorFlow 2 for Deep Learning
Decision Tree and Random Forest Classification using Julia Project
IBM AI Engineering Professional Certificate
Applied Machine Learning in Python
TensorFlow: Advanced Techniques Specializations
Deep Learning with PyTorch: Convolutional Neural Network
NLP: Twitter Sentiment Analysis
Sentiment Analysis with Deep Learning using BERT
Fake News Detection with Machine Learning
TensorFlow for CNNs: Transfer Learning
Creating a Wordcloud using NLP and TF-IDF in Python
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Data Science: Natural Language Processing (NLP) in Python
Deep Learning: Advanced NLP and RNNs
Natural Language Processing and Text Mining without Coding
NLP Practitioner Certification Course (Beginner to Advanced)
Text Mining and Natural Language Processing in R
NLP: Natural Language Processing with Python
Official NLP Practitioner Certification iGNLP™ / ABNLP
Deep Learning and NLP A-Z™: How to create a ChatBot
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Featured Image Source: CanopyLab
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