Even more famous than “the Japanese dude who won the hot dog eating contest” is “the French lady who lived to be 122 years old.”

Over the recent number of years, neural networks have come to play an increasingly central role in natural language processing. Owing in large part to milestones such as word embeddings, and the explosion of chatbots powered by language models built, at least in part, by neural networks, the achievements of neural networks in the domain are come increasingly quickly. Trying to keep up with these advancements can be troublesome. That’s where the today’s spotlighted resource comes in.

By Reinforce ConferenceSponsored Post.2018 was another hot year for Data Science and AI. Here we picked out 5 highlights, which in our opinion shaped the field in the past year. Comment below and let us know how you would pick!

In our next MünsteR R-user group meetup on Tuesday, February 5th, 2019, titled Don’t reinvent the wheel: making use of shiny extension packages., Suthira Owlarn will introduce the shiny package and show how she used it to build an interactive web app for her sequencing datasets.

Null hypothesis significance testing remains popular despite decades of concern about misuse and misinterpretation. We believe that much of the problem is due to language: significance testing has little to do with other meanings of the word ‘significance’. Despite the limitations of null-hypothesis tests, we argue here that they remain useful in many contexts as a guide to whether a certain effect can be seen clearly in that context (e.g. whether we can clearly see that a correlation or between-group difference is positive or negative). We therefore suggest that researchers describe the conclusions of null-hypothesis tests in terms of statistical ‘clarity’ rather than statistical ‘significance’. This simple semantic change could substantially enhance clarity in statistical communication. I can see clearly now: reinterpreting statistical significance

1. Advanced Modeling

before first name to set correct space above name in Outlook –>

The suite of AzureR packages for interfacing with Azure services from R is now available on CRAN. If you missed the earlier announcements, this means you can now use the install.packages function in R to install these packages, rather than having to install from the Github repositories. Updated versions of these packages will also be posted to CRAN, so you can get the latest versions simply by running update.packages.

In recent years, DBpedia, Freebase, OpenCyc, Wikidata, and YAGO have been published as noteworthy large, cross-domain, and freely available knowledge graphs. Although extensively in use, these knowledge graphs are hard to compare against each other in a given setting. Thus, it is a challenge for researchers and developers to pick the best knowledge graph for their individual needs. In our recent survey, we devised and applied data quality criteria to the above-mentioned knowledge graphs. Furthermore, we proposed a framework for finding the most suitable knowledge graph for a given setting. With this paper we intend to ease the access to our in-depth survey by presenting simplified rules that map individual data quality requirements to specific knowledge graphs. However, this paper does not intend to replace our previously introduced decision-support framework. For an informed decision on which KG is best for you we still refer to our in-depth survey. Which Knowledge Graph Is Best for Me?