Modelling semantic similarity plays a fundamental role in lexical semantic applications. A natural way of calculating semantic similarity is to access handcrafted semantic networks, but similarity prediction can also be anticipated in a distributional vector space. Similarity calculation continues to be a challenging task, even with the latest breakthroughs in deep neural language models. We first examined popular methodologies in measuring taxonomic similarity, including edge-counting that solely employs semantic relations in a taxonomy, as well as the complex methods that estimate concept specificity. We further extrapolated three weighting factors in modelling taxonomic similarity. To study the distinct mechanisms between taxonomic and distributional similarity measures, we ran head-to-head comparisons of each measure with human similarity judgements from the perspectives of word frequency, polysemy degree and similarity intensity. Our findings suggest that without fine-tuning the uniform distance, taxonomic similarity measures can depend on the shortest path length as a prime factor to predict semantic similarity; in contrast to distributional semantics, edge-counting is free from sense distribution bias in use and can measure word similarity both literally and metaphorically; the synergy of retrofitting neural embeddings with concept relations in similarity prediction may indicate a new trend to leverage knowledge bases on transfer learning. It appears that a large gap still exists on computing semantic similarity among different ranges of word frequency, polysemous degree and similarity intensity.

This paper designs a novel lexical hub to disambiguate word sense, using both syntagmatic and paradigmatic relations of words. It only employs the semantic network of WordNet to calculate word similarity, and the Edinburgh Association Thesaurus (EAT) to transform contextual space for computing syntagmatic and other domain relations with the target word. Without any back-off policy the result on the English lexical sample of SENSEVAL-2 1 shows that lexical cohesion based on edge-counting techniques is a good way of unsupervisedly disambiguating senses.

Background: For patients with advanced cancer or patients who have undergone digestive tract reconstruction, enteral nutrition is the most important nutritional support therapy, which can reduce the risk of enteral infection and improve self-immunity; while digital subtraction angiography (DSA) guided nasoenteric tube placement is suitable for nutritional support and palliative treatment of most patients with advanced cancer, many doctors because the preoperative preparation is not sufficient or the intraoperative operation is not standardized, resulting in catheter failure can not achieve the purpose of nutritional supply, and we need to summarize the lessons of failure and optimize the catheterization strategy.Methods: From February 2015 to July 2020, A total of 3,810 cases were treated with DSA guided nasoenteric feeding tube placement. According to the requirements that enteral nutrition could not be performed by the initial catheterization, 94 cases of catheterization failure were selected as the study subjects.The causes of catheterization failure were analyzed and summarized by analyzing the intraoperative image data and operation process; 42 cases of catheterization failure experienced successful catheterization after re-catheterization. By studying the relevant preoperative preparation and intraoperative operation, the treatment strategies and operation methods for successful re-catheterization were summarized.Results: In 94 patients with primary catheterization failure, anastomotic stenosis or obstruction accounted for 20.2%, excessive dilatation of gastric lumen accounted for 17.0%, pyloric stenosis or obstruction of antrum accounted for 13.8%, efferent loop stenosis or obstruction accounted for 11.7%, and the above factors were the main causes of DSA guided feeding tube failure; of the 42 patients with successful recatheterization, 9 patients underwent adequate negative pressure drainage before surgery, 7 patients modified the projection angle by adjusting the C-arm, 5 patients applied cone-beam CT technique, 5 patients used balloon dilatation of the stenotic segment, and the above factors were the main strategy methods for successful recatheterization. Conclusions:The success rate of DSA guided nasoenteric feeding tube placement will be greatly improved by adequate gastrointestinal decompression and drainage and other related preoperative preparation as well as good intraoperative application of cone-beam CT technique or combined application of balloon, gastroscope, stent and other technical means.

Background: Malignant intestinal obstruction refers to intestinal obstruction caused by advanced primary tumors or secondary metastatic malignant tumors. Because surgical treatment cannot significantly improve the life cycle, non-surgical treatment is mostly used to improve the symptoms of intestinal obstruction; transanal intestinal obstruction catheter and transnasal intestinal obstruction catheter are palliative therapies for decompression and drainage. Transanal intestinal obstruction catheter is mostly used for rectal and left colon obstruction. Transnasal intestinal obstruction catheter is mostly used for small intestinal obstruction.The two catheters are generally used alone according to the site of obstruction and clinical manifestations, and there are few reports on the combined use of the two catheters. We try to use the two catheters to treat patients with complex conditions and explore a better treatment strategy for malignant intestinal obstruction.

Uncertainty research is one of the critical problems in artificial intelligence. In an uncertain environment, a large quantity of information is expressed in linguistic values. Aiming at the missing linguistic-valued information, we first propose incomplete fuzzy linguistic formal context and then discuss the fuzzy linguistic approximate concept. Our proposal can describe the attributes of objects from two aspects simultaneously. One is an object's essential attributes, and another includes the essential and possible attributes. As a result, more object-related information can be obtained to reduce information loss effectively. We design a similarity metric for correcting the errors caused by the initial complement operation. We then construct a corresponding fuzzy linguistic approximate concept lattice for the task of approximate information retrieval. Finally, we illustrate the applicability and feasibility of the proposed approach with concrete examples, which clearly show that our approach can better deal with the linguistic-valued information in an uncertain environment.