Ambiguity is ubiquitous in natural language. Resolving ambiguous meanings is especially important in information retrieval tasks. While word embeddings carry semantic information, they fail to handle ambiguity well. Transformer models have been shown to handle word ambiguity for complex queries, but they cannot be used to identify ambiguous words, e.g. for a 1-word query. Furthermore, training these models is costly in terms of time, hardware resources, and training data, prohibiting their use in specialized environments with sensitive data. Word embeddings can be trained using moderate hardware resources. This paper shows that applying DBSCAN clustering to the latent space can identify ambiguous words and evaluate their level of ambiguity. An automatic DBSCAN parameter selection leads to high-quality clusters, which are semantically coherent and correspond well to the perceived meanings of a given word.

AstraZeneca's Dave Fredrickson discusses how the COVID-19 pandemic helped to bolster early cancer diagnosis from lung scans. Scientists have found that artificial intelligence could be an effective tool in predicting pancreatic cancer before a single symptom appears, according to a study published in the journal Nature Medicine on May 8. A team of researchers led by Copenhagen University Hospital in Denmark and Harvard Medical School in Boston completed a sweeping study to determine whether AI could flag a person's risk of developing the disease. The results exceeded their expectations, with the model successfully predicting risk up to three years before diagnosis. In 2023, about 64,050 people in the U.S. will be diagnosed with pancreatic cancer and about 50,550 will die from the aggressive disease, the American Cancer Society (ACS) says.

We present an implemented approach to transform natural language sentences into SPARQL, using background knowledge from ontologies and lexicons. Therefore, eligible technologies and data storage possibilities are analyzed and evaluated. The contributions of this paper are twofold. Firstly, we describe the motivation and current needs for a natural language access to industry data. We describe several scenarios where the proposed solution is required.

It's a booming time to be a truck driver. According to data NPR compiled from the US Census Bureau, truck driving is currently the most popular job in 29 states. It's not that truck driving is a particularly sought after career path, however. Rather, it is simply one that is available and pays decently. Unlike a plethora of other jobs that have declined in recent years, truck driving has remained immune to the forces that have elbowed out different lines of work.