This article discusses the challenges to meaning representation of terms posed by a quantum theory of terms (QTT) that was recently reported. We first summarize this theory and then highlight the difficulties of representing quanterms, which is the name we coined for the view that the QTT has of terms as quantum systems by analogy with quantum objects in quantum mechanics. We briefly summarize the representation practices followed to date to record and represent terminology. We use findings reported in the literature to model both terms and quanterms and found that current representations of terms in specialized repositories are collapsed quanterms at the expense of other states of the original quanterm. In this work, both quanterms and collapsed quanterms are mathematically modelled following formulations used in quantum mechanics. These formulations suggest that representations of quanterms need to include information about the probabilities of quanterm states and the role they play in the entanglement of terms for phenomena such as specialized collocations.

Named entity recognition (e.g., disease mention extraction) is one of the most relevant tasks for data mining in the medical field. Although it is a well-known challenge, the bulk of the efforts to tackle this task have been made using clinical texts commonly written in English. In this work, we present our contribution to the SocialDisNER competition, which consists of a transfer learning approach to extracting disease mentions in a corpus from Twitter written in Spanish. We fine-tuned a model based on mBERT and applied post-processing using regular expressions to propagate the entities identified by the model and enhance disease mention extraction. Our system achieved a competitive strict F1 of 0.851 on the testing data set.