semantic retrieval
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NeuroImage ◽  
2022 ◽  
Vol 246 ◽  
pp. 118760
Author(s):  
Meichao Zhang ◽  
Upasana Nathaniel ◽  
Nicola Savill ◽  
Jonathan Smallwood ◽  
Elizabeth Jefferies

Electronics ◽  
2022 ◽  
Vol 11 (2) ◽  
pp. 275
Author(s):  
Ljiljana Šerić ◽  
Antonia Ivanda ◽  
Marin Bugarić ◽  
Maja Braović

This paper presents a semantic conceptual framework and definition of environmental monitoring and surveillance and demonstrates an ontology implementation of the framework. This framework is defined in a mathematical formulation and is built upon and focused on the notation of observation systems. This formulation is utilized in the analysis of the observation system. Three taxonomies are presented, namely, the taxonomy of (1) the sampling method, (2) the value format and (3) the functionality. The definition of concepts and their relationships in the conceptual framework clarifies the task of querying for information related to the state of the environment or conditions related to specific events. This framework aims to make the observation system more queryable and therefore more interactive for users or other systems. Using the proposed semantic conceptual framework, we derive definitions of the distinguished tasks of monitoring and surveillance. Monitoring is focused on the continuous assessment of an environment state and surveillance is focused on the collection of all data relevant for specific events. The proposed mathematical formulation is implemented in the format of the computer readable ontology. The presented ontology provides a general framework for the semantic retrieval of relevant environmental information. For the implementation of the proposed framework, we present a description of the Intelligent Forest Fire Video Monitoring and Surveillance system in Croatia. We present the implementation of the tasks of monitoring and surveillance in the application domain of forest fire management.


2022 ◽  
Author(s):  
Nicholas E. Souter ◽  
Sara Stampacchia ◽  
Glyn Hallam ◽  
Hannah Thompson ◽  
Jonathan Smallwood ◽  
...  

2021 ◽  
Author(s):  
Daniela Mertzen ◽  
Dario Paape ◽  
Brian Dillon ◽  
Ralf Engbert ◽  
Shravan Vasishth

A long-standing debate in the sentence processing literature concerns the time course of syntactic and semantic information in online sentence comprehension. The default assumption in cue-based models of parsing is that syntactic and semantic retrieval cues simultaneously guide dependency resolution. When retrieval cues match multiple items in memory, this leads to similarity-based interference. Both semantic and syntactic interferencehave been shown to occur in English. However, the relative timing of syntactic vs. semantic interference remains unclear. In this first-ever cross-linguistic investigation of the time course of syntactic vs. semantic interference, the data from two eye-tracking reading experiments (English and German) suggest that the two types of interference can in principle arise simultaneously during retrieval. However, the data also indicate that semantic cues may be evaluated with a small timing lag in German compared to English. This suggests that there may be cross-linguistic variation in how syntactic and semantic cues are used to resolve linguistic dependencies in real-time.


2021 ◽  
Author(s):  
Nicholas E. Souter ◽  
Xiuyi Wang ◽  
Hannah Thompson ◽  
Katya Krieger-Redwood ◽  
Ajay D. Halai ◽  
...  

AbstractPatients with semantic aphasia have impaired control of semantic retrieval, often accompanied by executive dysfunction following left hemisphere stroke. Many but not all of these patients have damage to the left inferior frontal gyrus, important for semantic and cognitive control. Yet semantic and cognitive control networks are highly distributed, including posterior as well as anterior components. Accordingly, semantic aphasia might not only reflect local damage but also white matter structural and functional disconnection. Here we characterise the lesions and predicted patterns of structural and functional disconnection in individuals with semantic aphasia and relate these effects to semantic and executive impairment. Impaired semantic cognition was associated with infarction in distributed left- hemisphere regions, including in the left anterior inferior frontal and posterior temporal cortex. Lesions were associated with executive dysfunction within a set of adjacent but distinct left frontoparietal clusters. Performance on executive tasks was also associated with interhemispheric structural disconnection across the corpus callosum. Poor semantic cognition was associated with small left-lateralized structurally disconnected clusters, including in the left posterior temporal cortex. These results demonstrate that while left- lateralized semantic and executive control regions are often damaged together in stroke aphasia, these deficits are associated with distinct patterns of structural disconnection, consistent with the bilateral nature of executive control and the left-lateralized yet distributed semantic control network.


2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Zhengzhen An ◽  
Yue Zhao ◽  
Yanfei Zhang ◽  
Xuguang Li

Mineral resources are indispensable in the development of human society and are the foundation of national economic development. As the prospecting target shifts from outcrop ore to concealed ore, from shallow to deep, the difficulty of prospecting becomes more and more difficult. Therefore, the prediction of mineralization prospects is of great significance. This paper is aimed at completing the prediction of mineralization prospects by constructing geological semantic models and using mobile computer learning to improve the accuracy of prediction of mineralization prospects and expanding the application of semantic mobile computing. We use five different semantic relations to build a semantic knowledge library, realize semantic retrieval, complete information extraction of geological text data, and study mineral profiles. Through the distributed database of mobile computing, the association rules and random forest algorithm are used to describe the characteristics of minerals and the ore-controlling elements, find the association rules, and finally combine the geological and mineral data of the area and use the random forest algorithm to realize the prospect of mineralization district forecast. The geological semantic model constructed in the article uses the knowledge library for associative search to achieve an accuracy rate of 87.9% and a recall rate of 96.5%. The retrieval effect is much higher than that of traditional keyword retrieval methods. The maximum value of the posterior result of the mineralization prospect is 0.9027, the average value is 0.0421, and the standard deviation is 0.1069. The picture is brighter, and the probability of mineralization is higher.


2021 ◽  
pp. 108705472110605
Author(s):  
Brandy L. Callahan ◽  
Nayani Ramakrishnan ◽  
Prathiba Shammi ◽  
Daniel Bierstone ◽  
Rebecca Taylor ◽  
...  

Objective: Some features of attention-deficit/hyperactivity disorder (ADHD) may resemble those of mild cognitive impairment (MCI) in older adults, contributing to diagnostic uncertainty in individuals seeking assessment in memory clinics. We systematically compared cognition and brain structure in ADHD and MCI to clarify the extent of overlap and identify potential features unique to each. Method: Older adults from a Cognitive Neurology clinic (40 ADHD, 29 MCI, 37 controls) underwent neuropsychological assessment. A subsample ( n = 80) underwent structural neuroimaging. Results: Memory was impaired in both patient groups, but reflected a storage deficit in MCI (supported by relatively smaller hippocampi) and an encoding deficit in ADHD (supported by frontal lobe thinning). Both groups displayed normal executive functioning. Semantic retrieval was uniquely impaired in MCI. Conclusion: Although ADHD has been proposed as a dementia risk factor or prodrome, we propose it is rather a pathophysiologically-unique phenotypic mimic acting via overlap in memory and executive performance.


2021 ◽  
pp. 1-14
Author(s):  
Manon Hendriks ◽  
Wendy van Ginkel ◽  
Ton Dijkstra ◽  
Vitória Piai

Abstract Idioms can have both a literal interpretation and a figurative interpretation (e.g., to “kick the bucket”). Which interpretation should be activated can be disambiguated by a preceding context (e.g., “The old man was sick. He kicked the bucket.”). We investigated whether the idiomatic and literal uses of idioms have different predictive properties when the idiom has been biased toward a literal or figurative sentence interpretation. EEG was recorded as participants performed a lexical decision task on idiom-final words in biased idioms and literal (compositional) sentences. Targets in idioms were identified faster in both figuratively and literally used idioms than in compositional sentences. Time–frequency analysis of a prestimulus interval revealed relatively more alpha–beta power decreases in literally than figuratively used idiomatic sequences and compositional sentences. We argue that lexico-semantic retrieval plays a larger role in literally than figuratively biased idioms, as retrieval of the word meaning is less relevant in the latter and the word form has to be matched to a template. The results are interpreted in terms of context integration and word retrieval and have implications for models of language processing and predictive processing in general.


2021 ◽  
Author(s):  
Qingwen Tian ◽  
Shixing Zhou ◽  
Yu Cheng ◽  
Jianxia Chen ◽  
Yi Gao ◽  
...  

Knowledge Graph is a semantic network that reveals the relationship between entities, which construction is to describe various entities, concepts and their relationships in the real world. Since knowledge graph can effectively reveal the relationship between the different knowledge items, it has been widely utilized in the intelligent education. In particular, relation extraction is the critical part of knowledge graph and plays a very important role in the construction of knowledge graph. According to the different magnitude of data labeling, entity relationship extraction tasks of deep learning can be divided into two categories: supervised and distant supervised. Supervised learning approaches can extract effective entity relationships. However, these approaches rely on labeled data heavily resulting in the time-consuming and laborconsuming. The distant supervision approach is widely concerned by researchers because it can generate the entity relation extraction automatically. However, the development and application of the distant supervised approach has been seriously hindered due to the noises, lack of information and disequilibrium in the relation extraction tasks. Inspired by the above analysis, the paper proposes a novel curriculum points relationship extraction model based on the distant supervision. In particular, firstly the research of the distant supervised relationship extraction model based on the sentence bag attention mechanism to extract the relationship of curriculum points. Secondly, the research of knowledge graph construction based on the knowledge ontology. Thirdly, the development of curriculum semantic retrieval platform based on Web. Compared with the existing advanced models, the AUC of this system is increased by 14.2%; At the same time, taking "big data processing" course in computer field as an example, the relationship extraction result with F1 value of 88.1% is realized. The experimental results show that the proposed model provides an effective solution for the development and application of knowledge graph in the field of intelligent education.


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