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Lexalytics Celebrates Its Anniversary: 20 Years of NLP Innovation

Lexalytics

We’ve pioneered a number of industry firsts, including the first commercial sentiment analysis engine, the first Twitter/microblog-specific text analytics in 2010, the first semantic understanding based on Wikipedia in 2011, and the first unsupervised machine learning model for syntax analysis in 2014.

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From checkers to chess: A brief history of IBM AI

IBM Journey to AI blog

An early hint of today’s natural language processing (NLP), Shoebox could calculate a series of numbers and mathematical commands spoken to it, creating a framework used by the smart speakers and automated customer service agents popular today. In a televised Jeopardy!

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Revolutionizing Your Device Experience: How Apple’s AI is Redefining Technology

Unite.AI

Over the past decade, advancements in machine learning, Natural Language Processing (NLP), and neural networks have transformed the field. Apple introduced Siri in 2011, marking the beginning of AI integration into everyday devices. Notable acquisitions include companies like Xnor.a

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Why You Should Do NLP Beyond English

Sebastian Ruder

Natural language processing (NLP) research predominantly focuses on developing methods that work well for English despite the many positive benefits of working on other languages. Most of the world's languages are spoken in Asia, Africa, the Pacific region and the Americas. Each feature has 5.93

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From text to dream job: Building an NLP-based job recommender at Talent.com with Amazon SageMaker

AWS Machine Learning Blog

Founded in 2011, Talent.com is one of the world’s largest sources of employment. With over 30 million jobs listed in more than 75 countries, Talent.com serves jobs across many languages, industries, and distribution channels.

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Ryan Kolln, CEO at Appen – Interview Series

Unite.AI

On completion of an MBA from New York University, Ryan joined The Boston Consulting Group (BCG) in 2011 as a strategy consultant. His professional career began as an engineer, with a focus on mobile network data engineering in Australia, Asia and North America. This strategic vision is crucial as AI continues to transform industries globally.

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Understanding the different types and kinds of Artificial Intelligence

IBM Journey to AI blog

In other words, traditional machine learning models need human intervention to process new information and perform any new task that falls outside their initial training. For example, Apple made Siri a feature of its iOS in 2011. This early version of Siri was trained to understand a set of highly specific statements and requests.