Does a Neural Model Understand the De Re / De Dicto Distinction?

Neural network language models (NNLMs) are often casually said to understand language, but what linguistic structures do they really learn? We pose this question in the context of de re / de dicto ambiguities. Nouns and determiner phrases in intensional contexts, such as belief, desire, and modality, are subject to referential ambiguities. The phrase Lilo believes an alien is on the loose,\u27\u27 for example, has two interpretations: one ( de re ) in which she believes a specific entity which happens to be an alien is on the loose, and another ( de dicto ) in which she believes some unspecified alien is on the loose. In this paper we confront an NNLM with contexts producing de re / de dicto ambiguities. We use coreference resolution to investigate which interpretive possibilities the model captures. We find that while RoBERTa is sensitive to the fact that intensional predicates and indefinite determiners each change coreference possibilities, it does not grasp how the two interact with each other, and hence misses a deeper level of semantic structure. This inquiry is novel in its cross-disciplinary approach to philosophy, semantics and NLP, bringing formal semantic insight to an active research area testing the nature of NNLMs\u27 linguistic understanding

Enterolithiasis complicating eosinophilic enteritis: A case report and review of literature

We report a case of eosinophilic enteritis involving the proximal small bowel, a relatively rare entity, presenting unusually as enteroliths in a 68-year-old man with complaints of anemia, malena and abdominal pain. The disease if diagnosed in the initial stages responds well to medical treatment but if associated with complications or misdiagnosed, surgical modality is the treatment of choice. In our case, the patient presented with enteroliths and strictures. Resection and anastomosis of the small bowel containing stones was carried out. Histopathology confirmed the diagnosis as eosinophilic enteritis

Study of internet addiction in children with attention-deficit hyperactivity disorder and normal control

Background: In the current era, the use of electronic media in the form of Internet has increased exponentially, particularly among children, and has led to their excessive involvement in Internet. In this context, attention-deficit hyperactivity disorder (ADHD) children were found to have increased tendency for this addiction. Aims and Objectives: The aim is to study and compare Internet addiction between ADHD and normal children and the relation of demographic profile to Internet addiction. Materials and Methods: This was a cross-sectional study including 100 children (50 ADHD cases and 50 normal children without any psychiatric illness as controls) between the ages of 8 and 16 years. A semi-structured pro forma for demographic profile and Internet usage using Young's Internet Addiction Test (YIAT) was used. Statistical analysis was done using SPSS 20. Results: Internet addiction among ADHD children was 56% (54% having “probable Internet addiction” and 2% having “definite Internet addiction”). This was statistically significant (P < 0.05) in comparison with normal children where only 12% had Internet addiction (all 12% had “probable Internet addiction”). ADHD children were 9.3 times more prone to the development of Internet addiction as compared to normal (odds ratio – 9.3). Significant increase in average duration of Internet usage in ADHD children with increasing score of YIAT (P < 0.05) was seen. The incidence of Internet addiction was more in male ADHD children as compared to normal (P < 0.05). Conclusions: ADHD children are more prone to Internet addiction as compared to normal children and thus require preventive strategies