The task of named entity recognition can be transformed into a machine reading comprehension by associating the query and its context, which contains information, with encoding layer. In this process, model learns priori knowledge about entity, from query, to achieve good results. However, as length context increases, struggles an increasing number less relevant words, distract it task. Althoug...

Expressive clinical terminologies are of utmost importance for achieving a semantically interoperable data exchange and reuse in healthcare. SNOMED CT, widely respected as the most comprehensive terminology medicine, provides formal concept definitions based on description logic which not only allows advanced querying SNOMED-CT-coded but also flexibly augmenting its 350,000 concepts by allowing...

We describe a novel approach to statistical machine translation that combines syntactic information in the source language with recent advances in phrasal translation. This method requires a source-language dependency parser, target language word segmentation and an unsupervised word alignment component. We align a parallel corpus, project the source dependency parse onto the target sentence, e...

Metaphors such as icy stare depict social exclusion using cold-related concepts; they are not to be taken literally and certainly do not imply reduced temperature. Two experiments, however, revealed that social exclusion literally feels cold. Experiment 1 found that participants who recalled a social exclusion experience gave lower estimates of room temperature than did participants who recalle...

Quality is a complicated and multifarious topic in contemporary Linked Data research. The aspect of literal quality in particular has not yet been rigorously studied. Nevertheless, analyzing and improving the quality of literals is important since literals form a substantial (one in seven statements) and crucial part of the Semantic Web. Specifically, literals allow infinite value spaces to be ...