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.
What is Natural Language Processing (NLP)?
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.
Two Important Fuctions of NLP
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:
- Customer satisfaction: given data on customers, e.g. customer reviews, sentiment analysis identifies the moods and opinions of the customers.
- Credibility of news
- Concept/Entity Extraction
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
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:
- Spell checks (e.g. Grammarly)
- Text classification
- Automatic summary generation
- Language identification
- Sentiment analysis
- Market intelligence
- Virtual assistance (e.g. Alexa and Siri)
- Automated language translation (e.g. Google Translate, Microsoft/Skype Translator)
How does NLP work?
NLP engines rely on the following elements in order to process queries –
- Intent – The central concept of constructing a conversational user interface and is identified as the task a user wants to achieve or the problem statement a user is looking to solve.
- Utterance – The various different instances of sentences that a user may give as input to the chatbot as when they are referring to an intent.
- Entity. They include all characteristics and details pertinent to the user’s intent. This can range from location, date, time, etc.
- Context. This helps in saving and share different parameters over the entirety of the user’s session.
- Session. This essentially covers the start and endpoints of a user’s conversation.
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:
- Medicine – Summarized physicians’ notes for billing; Interoperability (moving differently-formatted medical records across providers)
- Law – Improved and more relevant lookup/research for legal documents
- Financial Industries / Banking – Actionable insights based on sentiments world news or social media
Why do we need NLP?
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:
- Web Pages
- and so much more…
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.”
Human Language is Hard for Machines
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?”
NLP Demand and Job Market
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%.
What do NLP Engineers / Scientists do?
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:
- To engineer a device capable of understanding human language and completing an action.
- To create computers that can analyze and generate human languages, including speech functions.
- Build computer programs and applications to understand the spoken human language.
NLP Jobs and Salaries
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.
Top Skills for NLP Jobs in 2021
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.
- PySpark & SparkNLP
- Hugging Face
- Seq2seq (TensorFlow)
- Alexa API
- Other programming languages (e.g. R, Java, Julia)
How to Learn NLP (Ideal Learning Path)
Here is a typical structured learning path to learn NLP over 7 months:
- Python for Data Science
- Data Presentation & Analysis
- Linear Regression
- Logistic Regression
- Decision Tree Algorithm
- K-fold Cross Validation Singular Value Decomposition
Month-1: Getting Comfortable with Text Data
- Text Mining
- Regular Expressions
- Text Preprocessing
- Exploratory Analysis of Text Data
- Extraction of Meta Features for Text Data
Month-2: Computational Linguistics and Word Vectors
- Extracting Linguistic Features
- Text Representation in Vector Space
- Topic Modeling
- Information Extraction
Month-3: Deep Learning for NLP
- Neural Networks
- Optimization Algorithms
- Recurrent Neural Networks (RNN)
Month-4: Deep Learning Models for NLP
- RNNs for Text Classification
- Convolutional Neural Network (CNN) Models for NLP
Month-5: Sequential Modelling
- Language Modeling
- Sequence-to-Sequence Modeling
Month-6: Transfer Learning in NLP
- Pre-trainer Large Language Models
- Fine Tuning Pre-trained Models
Month-7: Chatbots and Audio Processing
- Chat Bots
- Audio Processing
Top Online Courses on Natural Language Processing (NLP)
Best Online Courses for NLP on Coursera
Best Online Courses for NLP on Udemy (with Discounts)
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