In this article, I will mostly concentrate on the Hearst patterns, implementation and usage for hypernym extraction. However, I will use Named Entity Recognition (NER) and a dataset of patents; so I recommend checking my previous post in this cycle. Why do we care about patterns in the context of NLP? Because they significantly reduce and simplifies work, basically, it is a simple model. Despite being in the era of Transformer Neural Networks, patterns still can be beneficial.

In scientometrics, scientific collaboration is often analyzed by means of co-authorships. An aspect which is often overlooked and more difficult to quantify is the flow of expertise between authors from different research topics, which is an important part of scientific progress. With the Topic Flow Network (TFN) we propose a graph structure for the analysis of research topic flows between scientific authors and their respective research fields. Based on a multi-graph and a topic model, our proposed network structure accounts for intratopic as well as intertopic flows. Our method requires for the construction of a TFN solely a corpus of publications (i.e., author and abstract information). From this, research topics are discovered automatically through non-negative matrix factorization. The thereof derived TFN allows for the application of social network analysis techniques, such as common metrics and community detection. Most importantly, it allows for the analysis of intertopic flows on a large, macroscopic scale, i.e., between research topic, as well as on a microscopic scale, i.e., between certain sets of authors. We demonstrate the utility of TFNs by applying our method to two comprehensive corpora of altogether 20 Mio. publications spanning more than 60 years of research in the fields computer science and mathematics. Our results give evidence that TFNs are suitable, e.g., for the analysis of topical communities, the discovery of important authors in different fields, and, most notably, the analysis of intertopic flows, i.e., the transfer of topical expertise. Besides that, our method opens new directions for future research, such as the investigation of influence relationships between research fields.

Every day, the world is flooded by millions of messages and statements posted on Twitter or Facebook. Social media platforms try to protect users' personal data, but there still is a real risk of misuse, including elections manipulation. Did you know, that only 13 posts addressing important or controversial topics for society are enough to predict one's political affiliation with a 0.85 F1-score? To examine this phenomenon, we created a novel universal method of semi-automated political leaning discovery. It relies on a heuristical data annotation procedure, which was evaluated to achieve 0.95 agreement with human annotators (counted as an accuracy metric). We also present POLiTweets - the first publicly open Polish dataset for political affiliation discovery in a multi-party setup, consisting of over 147k tweets from almost 10k Polish-writing users annotated heuristically and almost 40k tweets from 166 users annotated manually as a test set. We used our data to study the aspects of domain shift in the context of topics and the type of content writers - ordinary citizens vs. professional politicians.

Google processes more than 8.5 billion searches every day. That's more than 100,000 searches per second, thousands of which could lead a user to a purchase. It's no wonder, then, that 60% of marketers list SEO as their number one inbound marketing priority. But generating organic traffic comes with challenges. Google has hundreds of billions of webpages in its index, competing for the top spots on search result pages.

Research¹ shows that including videos in web pages can effectively improve user experiences, increase Search Engine Optimization (SEO), and catch readers further down the sales funnel. To help agents with their business through Compass' website, the Compass AI Content Intelligence (AI-CI) team wants to make it easy for them to generate and share videos. We leverage state-of-the-art AI technologies to create visual and textual content for the videos to be generated and leverage the close to metal rendering algorithms together with the cloud-based distributed computation system to render the videos efficiently. With our current automatic video generation feature, agents can create a video with a single click, or with just a few more clicks they can customize it. They can then quickly review videos that have been created for them.

I've made some fundamental modifications to my app which I've distributed via the Archive methodology in Xcode a number of instances earlier than. Nevertheless it isn't working this night as I'm offered with the beneath error: I've ran flutter construct iOS –release with no warnings or errors earlier than making an attempt to Archive in Xcode. I've additionally tried doing a flutter clear too. After I run Validate on the archive earlier than making an attempt to distribute the bundle it comes again with 0 errors. It is simply when I attempt to Distribute the bundle, it comes again with the above error and I am undecided why and even how one can go about diagnosing it. Can anybody please assist level me in the appropriate course?

QuestDB 6.2, our previous minor version release, introduced JIT (Just-in-Time) compiler for SQL filters. As we mentioned last time, the next step would be to parallelize the query execution when suitable to improve the execution time even further and that's what we're going to discuss and benchmark today. QuestDB 6.3 enables JIT compiled filters by default and, what's even more noticeable, includes parallel SQL filter execution optimization allowing us to reduce both cold and hot query execution times quite dramatically. Prior to diving into the implementation details and running some before/after benchmarks for QuestDB, we'll be having a friendly competition with two popular time series and analytical databases, TimescaleDB and ClickHouse. The purpose of the competition is nothing more but an attempt to understand whether our parallel filter execution is worth the hassle or not.

When organizations start with search engine optimization, they typically begin with a devoted search engine optimization specialist. This particular person's tasks are normally broad and embrace a spread of duties. The most important problem then is getting a content material group to care about search engine optimization. Simply as you want your dev group to grasp search engine optimization's priorities, it's important that you just encourage your content material professionals to assist your search engine optimization targets. As a result of groups exterior the search engine optimization group handle all different content material creation and enchancment, there's a danger of missed alternatives for brand new content material, superficial understanding of consumer necessities, and unintentional de-optimization of content material discovered through engines like google.

Vivaldi considerably improved the best way you handle a ton of tabs earlier this 12 months with its new two-level tabs characteristic (opens in new tab), which shows full-size tabs in a second tab bar. The corporate has gone a step additional by permitting you to rename and edit the tab stacks. It's a part of a broader set of updates introduced at this time as a part of the rollout of Vivaldi 5.3 on Android. The newest launch introduces a bunch of enhancements that make for higher tab administration, translation, and syncing of looking knowledge throughout your gadgets. Maybe essentially the most notable change within the replace is the power to call your tab stacks.

Using computing resources at the National Energy Research Scientific Computing Center (NERSC) at Lawrence Berkeley National Laboratory (Berkeley Lab), researchers at Argonne National Laboratory have succeeded in exploring important materials science questions and demonstrated progress using machine learning to solve difficult search problems. By adapting a machine-learning algorithm from board games such as AlphaGo, the researchers developed force fields for nanoclusters of 54 elements across the periodic table, a dramatic leap toward understanding their unique properties and proof of concept for their search method. The team published its results in Nature Communications in January. Depending on their scale--bulk systems of 100 nanometers versus nanoclusters of less than 100 nanometers--materials can display dramatically different properties, including optical and magnetic properties, discrete energy levels, and enhanced photoluminescence. These properties may lend themselves to new scientific and industry applications, and scientists can learn about them by developing force fields--computational models that estimate the potential energies between atoms in a molecule and between molecules--for each element or compound.