Our work on automatic metaphor identification has just been published in the Current Trends in Corpus Linguistics by Peter Lang.
(more…)The Nation in American Political Discourse
Author: crowintelligenceteam
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.
(more…)Graph Theory and Network Science for Natural Language Processing – Part 1
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, like generating more labeled data, visualizing language acquisition and shedding light on hidden biases in language. This series gives you tips on how to get started with graph and network theory, which Python tools to use, where to look for graph databases and how to visualize networks, finally we offer a few resources on Graph Neural Networks.
(more…)How to fuel your data-driven business with text data? – Part 2, Strategies and Tools
If data is the new oil, then getting and enriching data is like fracking and refining it, at least in the case of textual data. Our previous post introduced the basic idea of data gathering and annotation. Now we help you with the strategies and tools you can employ to fuel your algorithms.
(more…)How to fuel your data-driven business with text data? – Part 1, Data gathering and annotation
If data is the new oil, then getting and enriching your own data is like fracking and refining it, at least in the case of textual data. This post gives you an overall picture on how to think about gathering and labeling data. You also get some tips on what kind of business questions should be considered.
(more…)Spark NLP: State of the art natural language processing at scale
Natural language processing is a key component in many data science systems that must understand or reason about text. Common use cases include question answering, paraphrasing or summarization, sentiment analysis, natural language BI, language modeling, and disambiguation. This talk introduces the Spark NLP library – the most widely used NLP library in the enterprise, thanks to implementing production-grade, trainable, and scalable versions of state-of-the-art deep learning & transfer learning NLP research, as a permissive open-source library backed by a highly active community and team.
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