Data in the form of text is increasingly commonplace. Businesses have plenty of text-based surveys and emails to plow through, researchers often use social media posts for analysis, and so on. It should be no surprise that NLP is becoming a must-have skillset for data scientists looking to move the needle in regards to research or their career. As such, here are a few NLP applications that you should know how to do if you want to get a job in NLP.
- Natural Language Understanding (NLU)
NLU goes beyond just processing words as data – it’s designed for computers to really understand what the input or human is trying to say. Rather than turning words into codes, NLU takes human syntax, sentences, and structure and makes decisions based on the way a human speaks. This is often seen in question/answering processes, searches, chatbots, and other platforms where the average person asks questions.
- Sentiment Analysis
Sentiment analysis looks not just at what’s being said, but the emotion behind it. This is especially helpful for B2C businesses, such as looking at social media discussion in regards to a product. Are your customers happy with your latest product? Is there a correlation between a product and customer service? S.A. will help you there.
- Machine Translation
Machine translation is the process of using a machine to convert text from one language to another, such as through a translation app. While there are plenty of apps out there that provide direct translation, it’s attractive to see someone with the skills who can translate for specific domains, such as business or weather, to give a more fleshed out translation. Many apps just translate word-by-word, substituting them at a very standard level, and context/sentence structure can get lost in translation.
Chatbots are everywhere. Countless businesses, events, government sites, and so on all use chatbots to automate their interactions with customers to save time, money, and resources. Why pay someone to answer FAQs when a bot can give the same answer? Being able to develop these custom chatbots for a business will definitely catch a hiring manager’s eye. To get specific, developing Q&A chatbots are incredibly in-demand.
- Semantic Parsing
Just as humans need to parse through words subconsciously to understand their meaning, so do machines. Semantic parsing is the skill of converting natural language data into something that a machine can understand by its own terms. What good is your data if your models/machines can’t do anything with it?
Bonus: Industry Knowledge
Since NLP applications involve text-based data, it’s good to know the context of which the data involves. Words may have different meanings depending on the industry, so if you’re training a model, it’ll be good to know the subtle discrepancies between meanings. Plus, when you’re talking to the stakeholders, it’ll always look good when you can explain the dataset and results in their terms, not just in data science jargon.
Learn NLP Skills with Ai+
The ODSC on-demand training platform, Ai+ Training, offers a number of videos that will help you get up-to-date on the latest NLP applications, skills, tricks, tools, platforms, libraries, and research advancements. Here are a few standout talks:
An Introduction to Transfer Learning in NLP and HuggingFace Tools | Thomas Wolf, PhD | Chief Science Officer | Hugging Face
Natural Language Processing Case-studies for Healthcare Models: Veysel Kocaman | Lead Data Scientist and ML Engineer | John Snow Labs
Transform your NLP Skills Using BERT (and Transformers) in Real Life: Niels Kasch, PhD | Data Scientist and Founding Partner | Miner & Kasch
A Gentle Intro to Transformer Neural Networks: Jay Alammar | Machine Learning Research Engineer | jalammar.github.io
Level Up: Fancy NLP with Straightforward Tools: Kimberly Fessel, PhD | Senior Data Scientist, Instructor | Metis
Build an ML pipeline for BERT models with TensorFlow Extended – An end-to-end Tutorial: Hannes Hapke | Senior Machine Learning Engineer | SAP Concur
Natural Language Processing: Feature Engineering in the Context of Stock Investing: Frank Zhao | Senior Director, Quantamental Research | S&P Global
Transfer Learning in NLP: Joan Xiao, PhD | Principal Data Scientist | Linc Global
Developing Natural Language Processing Pipelines for Industry: Michael Luk, PhD | Chief Technology Officer | SFL Scientific
Deep Learning-Driven Text Summarization & Explainability: Nina Hristozova | Junior Data Scientist | Thomson Reuters