We’ve just started our shop where you can buy printouts of our vizs. We hope you’ll like the printed versions of our works.
(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.
(more…)Active Learning for Natural Language Processing
More than 90% of machine learning applications improve with human feedback. For example, a model that classifying news articles into pre-defined topics has been trained on 1000s of examples where humans have manually annotated the topics. However, if there are tens of millions of news articles, it might not be feasible to manually annotate even 1% of them. If we only sample randomly, we will mostly get popular topics like “politics” that the machine learning model can already identify accurately. So, we need to be smarter about how we sample. This talk is about “Active Learning”, the process of deciding what raw data is the most optimal for human review, covering: Uncertainty Sampling; Diversity Sampling; and some advanced methods like Active Transfer Learning.
(more…)Growth Hacking with NLP and Sentiment Analysis
We developed a course, Growth Hacking with NLP and Sentiment Analysis during the past months. We loved working with Manning, and now we are excited to start mentoring our students. Join us if you’d like to learn about applied sentiment analysis using Python and libraries like simpletransformers and scikit-learn.
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