Universal Semantic Annotator

Explicit semantic knowledge has often been considered a necessary ingredient to enable the development of intelligent systems. However, current stateof- the-art tools for the automatic extraction of such knowledge often require expert understanding of the complex techniques used in lexical and sentence-level semantics and their linguistic theories. To overcome this limitation and lower the barrier to entry, we present the Universal Semantic Annotator (USeA) ELG pilot project, which offers a transparent way to automatically provide high-quality semantic annotations in 100 languages through state-of-the-art models, making it easy to exploit semantic knowledge in real-world applications

Generating a Generation of Proteasome Inhibitors: From Microbial Fermentation to Total Synthesis of Salinosporamide A (Marizomib) and Other Salinosporamides

The salinosporamides are potent proteasome inhibitors among which the parent marine-derived natural product salinosporamide A (marizomib; NPI-0052; 1) is currently in clinical trials for the treatment of various cancers. Methods to generate this class of compounds include fermentation and natural products chemistry, precursor-directed biosynthesis, mutasynthesis, semi-synthesis, and total synthesis. The end products range from biochemical tools for probing mechanism of action to clinical trials materials; in turn, the considerable efforts to produce the target molecules have expanded the technologies used to generate them. Here, the full complement of methods is reviewed, reflecting remarkable contributions from scientists of various disciplines over a period of 7 years since the first publication of the structure of 1

Framing word sense disambiguation as a multi-label problem for model-agnostic knowledge integration

Recent studies treat Word Sense Disambiguation (WSD) as a single-label classification problem in which one is asked to choose only the best-fitting sense for a target word, given its context. However, gold data labelled by expert annotators suggest that maximizing the probability of a single sense may not be the most suitable training objective for WSD, especially if the sense inventory of choice is fine-grained. In this paper, we approach WSD as a multi-label classification problem in which multiple senses can be assigned to each target word. Not only does our simple method bear a closer resemblance to how human annotators disambiguate text, but it can also be extended seamlessly to exploit structured knowledge from semantic networks to achieve state-of-the-art results in English all-words WSD