We are presenting at the 9th Visual Learning Conference. Below, you can see a short teaser video on the main issues of our research. More to come on November 26.
(more…)Prototypes of Women in American Advertisements: A Machine Aided Image Analysis
Image metaphors of Trianon
Orsolya Putz’s recent paper, Image metaphors of Trianon, has been published in Cognitive Linguistic Studies.
(more…)Must-read Business Books for AI Practitioners – with Bonus Titles
It’s commonsense that AI applications are changing our life. We are in the midst of an AI hype with high expectations towards technology. Governments using AI are transforming our societies. More and more money has been poured into AI startups. These phenomena made us interested in the business side of AI, so we collected the hottest titles on the topic.
(more…)Graph Theory and Network Science for Natural Language Processing – Part 4, TextGraphs & Graph Neural Networks
We close our series on Graph Theory and Network Science for NLP with an overview of the recent developments in the field. It means deep learning enters the scene – finally.
(more…)The Nation in American Political Discourse
Our work on automatic metaphor identification has just been published in the Current Trends in Corpus Linguistics by Peter Lang.
(more…)How Jina Saves Your Time on Building Cloud-Native Neural Search Systems
Today, with the ever more long documents and multimedia data, finding the right information is more important and challenging than ever. The rise of deep learning has ushered in a new era of “neural search”. However, building a neural search system is non-trivial work for many engineers. The main challenges are: (1) long dev cycle due to the complex tech stack (2) poor scalability due to the glued-architecture (3) strong requirements on the domain knowledge to fine-tune the results. With Jina (https://github.com/jina-ai/jina), engineers can quickly build up a search engine powered by state-of-the-art AI in just minutes. In this talk, I will introduce the design philosophy and the key features of Jina; and showcase how Jina bootstraps a QA semantic search system and a short-video search system in just lines of code.
(more…)Choose a printed visualization from our shop
Graph Theory and Network Science for Natural Language Processing – Part 3, Visualization
If you believe that graph and network visualization is a kind of art, this post was written for you. If you believe that it isn’t, then you should also keep reading. Since we love using graph-based methods in our work, like generating more labeled data, visualizing language acquisition and shedding light on hidden biases in language, we started a series on graph theory and network science. The first part was devoted to the theoretical background of graphs and how to deal with them using Python, while the second part was about graph databases and analytics engines. Now we turn to graph and network visualization.
(more…)Using Knowledge Graphs to predict customer needs, improve product quality and save costs
Alessandro Negro of GraphAware and author of Graph-Powered Machine Learning was our speaker this week. He delivered a talk titled Using Knowledge Graphs to predict customer needs, improve product quality and save costs and presented a demo, Fighting corona virus with Knowledge Graph and Hume. You can watch the recordings of the meetup below.
(more…)Graph Theory and Network Science for Natural Language Processing – Part 2, Databases and Analytics Engines
From keyword extraction to knowledge graphs, graph and network science offer a good framework to deal with natural language. We love using graph-based methods in our work so much, like generating more labeled data, visualizing language acquisition and shedding light on hidden biases in language, that we decided to start a series on the topic. The first part explored the theoretical background of network science and dealt with graphs using Python. This part focuses on graph processing frameworks and graph databases.
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