No abstract

We propose a semantic tagger that provides high level concept information for phrases in clinical documents, which enriches medical information tracking system that support decision making or quality assurance of medical treatment. In this paper, we have tried to deal with patient records written by doctors rather than well-formed documents such as Medline abstracts. In addition, annotating clinical text on phrases semantically rather than syntactically has been attempted, which are at higher level granularity than words that have been the target for most tagging work.

Many studies have investigated the management of data delivered over sensor networks and attempted to standardize their relations. Sensor data come from numerous tangible and intangible sources, and existing work has focused on the integration and management of the sensor data itself. The data should be interpreted according to the sensor environment and related objects, even though the data type, and even the value, is exactly the same. This means that the sensor data should have semantic connections with all objects, and so a knowledge base that covers all domains should be constructed. In this paper, we suggest a method of domain terminology collection based on Wikipedia category information in order to prepare seed data for such knowledge bases. However, Wikipedia has two weaknesses, namely, loops and unreasonable generalizations in the category structure. To overcome these weaknesses, we utilize a horizontal bootstrapping method for category searches and domain-term collection. Both the categoryarticle and article-link relations defined in Wikipedia are employed as terminology indicators, and we use a new measure to calculate the similarity between categories. By evaluating various aspects of the proposed approach, we show that it outperforms the baseline method, having wider coverage and higher precision. The collected domain terminologies can assist the construction of domain knowledge bases for the semantic interpretation of sensor data.

This paper proposes a model for recognizing sentential paraphrases through Predicate-Argument Tuple (PAT)-based approximate alignment between two texts. We cast the paraphrase recognition problem as a binary classification by defining and applying various alignment features which could effectively express the semantic relatedness between two sentences. Experiment confirmed the potential of our approach and error analysis revealed various paraphrase patterns not being solved by our system, which can help us devise methods for further performance improvement.

Objective. The current study examined gender-related differences in hemispheric asymmetries of graph metrics, calculated from a cortical thickness-based brain structural covariance network named hemispheric morphological network. Methods. Using the T1-weighted magnetic resonance imaging scans of 285 participants (150 females, 135 males) retrieved from the Human Connectome Project (HCP), hemispheric morphological networks were constructed per participant. In these hemispheric morphologic networks, the degree of similarity between two different brain regions in terms of the distributed patterns of cortical thickness values (the Jensen–Shannon divergence) was defined as weight of network edge that connects two different brain regions. After the calculation and summation of global and local graph metrics (across the network sparsity levels K=0.10‐0.36), asymmetry indexes of these graph metrics were derived. Results. Hemispheric morphological networks satisfied small-worldness and global efficiency for the network sparsity ranges of K=0.10–0.36. Between-group comparisons (female versus male) of asymmetry indexes revealed opposite directionality of asymmetries (leftward versus rightward) for global metrics of normalized clustering coefficient, normalized characteristic path length, and global efficiency (all p<0.05). For the local graph metrics, larger rightward asymmetries of cingulate-superior parietal gyri for nodal efficiency in male compared to female, larger leftward asymmetry of temporal pole for degree centrality in female compared to male, and opposite directionality of interhemispheric asymmetry of rectal gyrus for degree centrality between female (rightward) and male (leftward) were shown (all p<0.05). Conclusion. Patterns of interhemispheric asymmetries for cingulate, superior parietal gyrus, temporal pole, and rectal gyrus are different between male and female for the similarities of the cortical thickness distribution with other brain regions. Accordingly, possible effect of gender-by-hemispheric interaction has to be considered in future studies of brain morphology and brain structural covariance networks.