Medical incident reports (MIRs) are documents that record what happened in a medical incident. A typical MIR consists of two sections: a structured categorical part and an unstructured text part. Most texts in MIRs describe what medication was intended to be given and what was actually given, because what happened in an incident is largely due to discrepancies between intended and actual medications. Recognizing the intention of clinicians and the factuality of medication is essential to understand the causes of medical incidents and avoid similar incidents in the future. Therefore, we are developing an MIR corpus with annotation of intention and factuality as well as of medication entities and their relations. In this paper, we present our annotation scheme with respect to the definition of medication entities that we take into account, the method to annotate the relations between entities, and the details of the intention and factuality annotation. We then report the annotated corpus consisting of 349 Japanese medical incident reports.

Dividing biomedical abstracts into several segments with rhetorical roles is essential for supporting researchers’ information access in the biomedical domain. Conventional methods have regarded the task as a sequence labeling task based on sequential sentence classification, i.e., they assign a rhetorical label to each sentence by considering the context in the abstract. However, these methods have a critical problem: they are prone to mislabel longer continuous sentences with the same rhetorical label. To tackle the problem, we propose sequential span classification that assigns a rhetorical label, not to a single sentence but to a span that consists of continuous sentences. Accordingly, we introduce Neural Semi-Markov Conditional Random Fields to assign the labels to such spans by considering all possible spans of various lengths. Experimental results obtained from PubMed 20k RCT and NICTA-PIBOSO datasets demonstrate that our proposed method achieved the best micro sentence-F1 score as well as the best micro span-F1 score.

Several recent studies have shown that textual information of user posts and user behaviors such as liking and sharing the specific posts are useful for predicting the personality of social media users. However, less attention has been paid to the textual information derived from the user behaviors. In this paper, we investigate the effect of textual information on user behaviors for personality prediction. Our experiments on the personality prediction of Twitter users show that the textual information of user behaviors is more useful than the co-occurrence information of the user behaviors. They also show that taking user behaviors into account is crucial for predicting the personality of users who do not post frequently.