Semantic parsing transforms a natural language question into a formal query over a knowledge base. Many existing methods rely on syntactic parsing like dependencies. However, the accuracy of producing such expressive formalisms is not satisfying on long complex questions. In this paper, we propose a novel skeleton grammar to represent the high-level structure of a complex question. This dedicated coarse-grained formalism with a BERT-based parsing algorithm helps to improve the accuracy of the downstream fine-grained semantic parsing. Besides, to align the structure of a question with the structure of a knowledge base, our multi-strategy method combines sentence-level and word-level semantics. Our approach shows promising performance on several datasets.

I have worked in the data industry for over seven years and had the privilege of designing, building, and deploying two recommender systems (RecSys) that went on to serve millions of customers. For each chapter, I will walk through case studies and share my experience designing RecSys. This is part of my Modern Visual RecSys series; feel free to check out the rest of the series at the end of the article. We begin with a case study of Spotify to understand how RecSys works and introduce several key concepts, including a modern approach called convolutional neural networks (CNN), applied to music. Let us take a look at my personalized music recommendations from Spotify. It contains a mix of Chinese/Japanese/ English pop music with new music and old tunes going back some 20 years.

The explosion of breakthroughs, investments, and entrepreneurial activity around artificial intelligence over the last decade has been driven exclusively by deep learning, a sophisticated statistical analysis technique for finding hidden patterns in large quantities of data. A term coined in 1955--artificial intelligence--was applied (or mis-applied) to deep learning, a more advanced version of an approach to training computers to perform certain tasks--machine learning--a term coined in 1959. The recent success of deep learning is the result of the increased availability of lots of data (big data) and the advent of Graphics Processing Units (GPUs), significantly increasing the breadth and depth of the data used for training computers and reducing the time required for training deep learning algorithms. The technology that animated movies like "Toy Story" and enabled a variety of special effects is the ... [ ] focus of this year's Turing Award, the technology industry's version of the Nobel Prize. The term "big data" first appeared in computer science literature in an October 1997 article by Michael Cox and David Ellsworth, "Application-controlled demand paging for out-of-core visualization," published in the Proceedings of the IEEE 8th conference on Visualization.

Ken Burns has spent the last 40 years chronicling the most poignant and influential events in American history. The 66-year-old Oscar-nominated filmmaker has crafted definitive and multifaceted histories of the Civil War, baseball, the Roosevelts, cancer, country music and jazz. In an age of short Tweets and shorter attention spans, Burns's films are sprawling, deep-dive studies on topics that simultaneously reveal the best and worst of America. We are living through one of those moments right now as the coronavirus shakes every aspect of American life. With most of the country stuck at home and weathering a torrent of fear and breaking news, Burns is offering an alternative.

All of the major social media companies and their parent corporations have issued a joint statement on their COVID-19 response efforts. They have also invited other companies to join them as they work to keep their communities healthy and safe. In the statement, the companies stressed their joint effort to combat fraud and misinformation about the virus, elevate authoritative content on their platforms, and share critical updates in coordination with the governments. The Finnish text analytics company Utopia Analytics is aware of the struggle that social media giants now face with content moderation. This is why Utopia Analytics is offering its Utopia AI Moderator service to one of these giants at cost for as long as the crisis lasts.

Talent departments are spending $250 billion on recruitment, interviewing and candidate assessment solutions. But as employers invest in these disparate technologies, they sacrifice efficiency and deliver a disconnected experience throughout the talent lifecycle. Phenom, a leader in Talent Experience Management (TXM), is a single platform that individualizes the end-to-end talent journey for candidates, recruiters, employees and management. The company recenlty released its report, Chatbots for Recruiting: 2020 Benchmarks, which analyzes more than 20 million chatbot interactions from over 100 Phenom Bot deployments. The report showcases how career sites with chatbots convert more applicants and nearly double the number of candidate leads.

Technavio is a leading global technology research and advisory company. Their research and analysis focus on emerging market trends and provides actionable insights to help businesses identify market opportunities and develop effective strategies to optimize their market positions. With over 500 specialized analysts, Technavio's report library consists of more than 17,000 reports and counting, covering 800 technologies, spanning across 50 countries. Their client base consists of enterprises of all sizes, including more than 100 Fortune 500 companies. This growing client base relies on Technavio's comprehensive coverage, extensive research, and actionable market insights to identify opportunities in existing and potential markets and assess their competitive positions within changing market scenarios.

Not only does this provide useful information to users in the moment, but it has also helped raise awareness and increase the adoption of Lexikon. Since launching the Lexikon Slack Bot, we've seen a sustained 25% increase in the number of Lexikon links shared on Slack per week. You just listened to a track by a new artist on your Discover Weekly and you're hooked. You want to hear more and learn about the artist. So, you go to the artist page on Spotify where you can check out the most popular tracks across different albums, read an artist bio, check out playlists where people tend to discover the artist, and explore similar artists.

This is the first part of a series by Levon Paradzhanyan that demystifies data science, machine learning, deep learning, and artificial intelligence down while explaining how they all tie into one another. Artificial Intelligence emerged in our lives many years ago. First, as science fiction and today embedded in real products. It has since been followed by newer buzzwords such as data science, machine learning, and deep learning. Yet there are many misconceptions related to these terms.