ライフサイエンスコーパス: semantic

1 ons (sentence processing, reading and verbal semantics).

2 rb's modification of the DO noun's activated semantics.

3 el, which represents graphs considering edge semantics.

4 distributional structure of natural language semantics.

5 onsistently associated with retrieval of the semantic (95% of eligible contrasts) than perceptual asp

6 dinal fasciculi) tracts were associated with semantic ability, while associations with phonological a

7 would contribute primarily to direct lexico-semantic access.

8 We estimate emotion semantics across a sample of 2474 spoken languages using

9 Premotor neurons can also contribute to "semantic" affordance processing, as they can discharge d

10 Standardization and semantic alignment have been considered one of the major

11 Semantic analyses of paper abstracts reveal that these l

12 Probabilistic latent semantic analysis (pLSA) is commonly applied to describe

13 Through network modeling and semantic analysis, we provide an initial exploration of

14 Analyses of the semantic and acoustic structure of the recognition of em

15 The purpose of this study is to develop a semantic and domain-specific method to enable constructi

16 Similar phonological, semantic and fluency-related components were found for P

17 a and regressed these matrices on indices of semantic and phonological ability derived from their res

18 also illuminate limbic contributions to both semantic and phonological processing for word production

19 t distinct neuroanatomical networks subserve semantic and phonological processing, respectively, the

20 Current algorithms use a variety of semantic and statistical approaches to prioritize the ty

21 oud mediated by the interplay between lexico-semantic and sublexical/phonological neurocognitive syst

22 graphy (EEG) and manipulated the presence of semantic and syntactic information apart from the timesc

23 These domains mainly reflect phonology, semantics and fluency; however, these studies did not ac

24 retrieval can be characterized in terms of "semantic" and "conceptual" factors that render chemical

25 he function of the ventral pathway (used for semantics), and vice-versa.

26 ferential diagnosis of nonfluent/agrammatic, semantic, and logopenic PPA variants.

27 associated with articulatory, phonological, semantic, and multimodal orthography-to-phonology proces

28 ch features from multiple domains (acoustic, semantic, and psycholinguistic) to assess mental states

29 l with the conceptual, the episodic with the semantic, and the concrete with the abstract.

30 The semantic annotation and the web tool we have developed i

31 Currently, no consensus protocol for semantic annotation exists among the larger biological m

32 Semantic annotation is a critical component for enhancin

33 However, realizing the potential benefits of semantic annotation requires the development of model an

34 -readable links to knowledge resource terms, semantic annotations describe the computational or biolo

35 To support this capability, we use semantic annotations to explicitly capture the underlyin

36 individual variability in response to lexico-semantic anomalies.

37 act by reducing the pathologically enhanced semantic, anxiety-related associations of patients with

38 sessed how the effective connectivity of the semantic appraisal network targeted by this disease was

39 with the retrieval of perceptual (97%), than semantic aspects of memory (43%).

40 a meta-analysis, with task labels related to semantic associations (sentence processing, reading and

41 his shows that the memory for object-context semantic associations is activated regardless of whether

42 eracting forms of learning and memory (e.g., semantic associative memory, Pavlovian conditioning, and

43 SemGen is a tool for semantics-based annotation and composition of biosimulat

44 a using data models with precise, computable semantics, but adoption of semantic standards for repres

45 Our results suggest that both semantic categories and relations are represented by spa

46 s of 24 novel words that shared phonemes and semantic categories but were written in different artifi

47 nts has found that neural representations of semantic categories, such as fruit, are shared across la

48 esults demonstrate successful and comparable semantic categorisation.

49 with a 6 Hz stream of images drawn from one semantic category except every fifth image (occurring at

50 nship between basic-level exemplars within a semantic category.

51 of 1.2 Hz) which was drawn from an alternate semantic category.

52 atically distinguish between images by their semantic category.

53 le advances in describing the linguistic and semantic changes that happen during the adult life span

54 specific features and represent generalized semantic characteristics (general semantic representatio

55 of the written word to both phonological and semantic codes.

56 population, it is crucial to design tests of semantic comprehension that are sensitive in individuals

57 nce contexts manipulating lexical/conceptual semantic congruity.

58 In all the experiments, we found a semantic consistency advantage for both context categori

59 udy suggests that the facilitation effect of semantic consistency on categorization occurs at the sta

60 English phrases that varied in the degree of semantic constraint that the modifier (W1) exerted on th

61 ssing accounts, we found early activation of semantic constraints in frontal cortex (LBA45) as W1 was

62 d functional magnetic resonance imaging with semantic content analyses to investigate the neural mech

63 gate the sensitivity of the brain regions to semantic content and the type of semantic representation

64 that it is likely that the extraction of the semantic content of the vocalizations of African wild do

65 measures is taken to be an indication of how semantic context leading up to a word influences how its

66 cently introduced method for quantifying the semantic context of speech and relate it to a commonly u

67 During natural speech comprehension, we use semantic context when processing information about new i

68 To this end, we present a preliminary semantic data model and use cases and competency questio

69 Our framework consists of two phases: (1) a semantic data-cleaning phase and (2) a domain-specific d

70 windows of peptides or proteins matching its semantic definition.

71 Semantic dementia (SD) is a neurodegenerative disorder c

72 ed upon neuropsychological investigations of semantic dementia (SD).

73 Semantic dementia formed a true diagnostic category (i.e

74 The syndrome of semantic dementia represents the "other side of the coin

75 integrative memory model through the lens of semantic dementia, we propose a number of important exte

76 We studied patients with semantic dementia-the paradigmatic disorder of the brain

77 nd reuse, tools and guidelines to facilitate semantic description of mathematical models, and reposit

78 umber of people, HMAX-C1), we found that the semantic dissimilarity structure was best captured by pa

79 ures that require human curation, and that a semantic distance measure derived from the Wikipedia art

80 thought, prompting a rethink of the episodic-semantic distinction.

81 The most-aligned words were from semantic domains with high internal structure (number, q

82 The notion of semantic embodiment posits that concepts are represented

83 We constructed an artificial semantic environment by parsing a bidimensional audiovis

84 Interreader variability in semantic feature annotation remains a challenge and affe

85 representation may be determined by specific semantic features (e.g. sensorimotor information), or ma

86 plied to automatically learn topological and semantic features for each protein in protein-protein in

87 een deep learning from "big data" (to create semantic features for individual words) and supervised l

88 itory features from deep neural networks and semantic features from a natural language processing mod

89 constrained for either animate or inanimate semantic features of upcoming nouns, and the broader dis

90 vidence for the prediction of coarse-grained semantic features that goes beyond the prediction of ind

91 representational spaces linked to particular semantic features.

92 ns of the semantic network were sensitive to semantic features: the left pMTG/ITG was sensitive to ha

93 ne aortic stiffness was associated with poor semantic fluency (B = -0.47, 95% CI -0.76 to -0.18, Bonf

94 deficits in working memory, phonological and semantic fluency, general intelligence quotient and redu

95 y associated with better memory function and semantic fluency, only in AD patients.

96 outcomes were performance on verbal memory, semantic fluency, working memory, and executive function

97 ot longitudinally associated with changes in semantic fluency.

98 Contemporary semantics has uncovered a sophisticated typology of ling

99 have demonstrated outstanding performance in semantic image segmentation tasks, large annotation data

100 eposition and by two key phenotypic factors, semantic impairment and behavioural disinhibition.

101 Semantic impairment and social disinhibition were linked

102 Here we show that the degree of generalised semantic impairment is related to the patients' total, b

103 , patients with svPPA manifest marked lexico-semantic impairments including difficulties in reading w

104 al variant frontotemporal dementia often had semantic impairments.

105 that the complex realm of bonding is full of semantic inconsistencies, and we invite experimentalists

106 s revealed the central roles of the creative semantic (inferior frontal, middle frontal and angular g

107 nd, which is a novel potential mechanism for semantic influences on reading.

108 In mature adults, the capacity to represent semantic information also correlated with higher levels

109 s in the human cerebral cortex, while amodal semantic information appears to be represented in a few

110 However, semantic information has played a vital role in propelli

111 Prior neuroimaging studies have shown that semantic information in spoken language is represented i

112 we show that although the representation of semantic information in the human brain is quite complex

113 olling for the acoustic-prosodic and lexical-semantic information in the signal.

114 We find that the capacity to represent semantic information is linked to higher naming accuracy

115 that a clear differentiation of spatial from semantic information is necessary to advance research in

116 nt of the sensory modality through which the semantic information is received.SIGNIFICANCE STATEMENT

117 ifferent dynamics depending on pragmatic and semantic information provided by the context in which th

118 that contrast acoustic-prosodic and lexical-semantic information to show that, during spoken languag

119 an object and/or in linking an object to its semantic information.

120 e containing hierarchy levels for cumulating semantic information.

121 e of spatial configurations independently of semantic information.

122 Temporal Lobe (rATL), putatively involved in semantic integration, is distinctively activated when pe

123 to ensure consistent reporting and maximises semantic interoperability.

124 suggest that the representation of language semantics is independent of the sensory modality through

125 n the right hemisphere; memory retrieval and semantic judgement in the left hemisphere.

126 ated with the participants' performance in a semantic judgment task, indicating the importance of thi

127 ether access to spatially related geographic semantic knowledge (1) involves the same domain-selectiv

128 typical features but also complex, atypical, semantic knowledge (e.g., "Pizza was invented in Naples"

129 offs between executive control and long-term semantic knowledge are linked to a person's tendency to

130 the processes underpinning lexical access to semantic knowledge may be sensitive to ageing.

131 er performance on tasks relying primarily on semantic knowledge, rather than executive control, was l

132 cortical representation of this more complex semantic knowledge.

133 emNet, thus confirming that it stores useful semantic knowledge.

134 the considerable flexibility of our complex semantic knowledge.SIGNIFICANCE STATEMENT We know not on

135 ey differed in both perceptual details and a semantic label.

136 phenotypic and trait data are available in a semantic language from knowledge bases, but these are of

137 characterization revealed associations with semantic language processing (left lateral prefrontal an

138 ategories, the data-driven model repositions semantics, language, social behaviour and face recogniti

139 ections between the orthographic and lexical-semantic levels of processing.

140 ion between the orthographic and the lexical-semantic levels of processing.

141 the local and global information of disease semantics (lncRNA functions) respectively.

142 of speech perception assume that, to extract semantic meaning, the signal is transformed into unknown

143 -order regions involved in the extraction of semantic meaning.

144 ption and knowledge and between episodic and semantic memory are not as clear cut as previously thoug

145 ommon novelty') activate the VTA and promote semantic memory formation via systems memory consolidati

146 al studies investigating the preservation of semantic memory in healthy ageing have reported mixed fi

147 c strategies and suggests the maintenance of semantic memory in healthy ageing.

148 It is therefore necessary to assess semantic memory utilising tasks that are not explicitly

149 The distinction between episodic and semantic memory was first proposed in 1972 by Endel Tulv

150 working memory, executive function, language/semantic memory, and global composite) using z-scores fo

151 e, we review recent research on episodic and semantic memory, highlighting similarities between the t

152 of episodic memory and its distinction from semantic memory.

153 tributed neuronal processing that underwrite semantic memory.

154 What role does the hippocampus play in semantic memory?

155 tivariate pattern analysis and computational semantic modeling to source-localized electro/magnetoenc

156 s deployed generic group-level computational semantic models to distinguish between neural representa

157 object representations, but not auditory or semantic models, suggesting representations of complex v

158 phonological language tasks (N = 21) and (2) semantic (N = 21) language tasks.

159 ant frontotemporal dementia (n = 77) and the semantic (n = 45) and non-fluent (n = 39) variants of pr

160 Here, we analysed the semantic neighbourhoods of 1,010 meanings in 41 language

161 Here, we demonstrate a method to build a semantic network from published scientific literature, w

162 The findings support a biased semantic network in panic disorder, which is normalized

163 l and neural correlates of the panic-related semantic network in patients with panic disorder.

164 frontal and supramarginal regions; a ventral semantic network involving anterior middle temporal and

165 of concepts, which have unique and extremal semantic network properties.

166 Finally, two brain regions of the semantic network were sensitive to semantic features: th

167 These anatomy-based gene networks were semantic networks, as they were constructed based on the

168 is not merely an exercise in clarifying the semantics of coexistence and neutral theories, but rathe

169 ing models-have been employed to capture the semantics of concepts.

170 fers Boolean rules based on the topology and semantics of molecular interaction maps built with CellD

171 ests a compensatory mechanism for processing semantic olfactory cues.

172 lationship but no orthographic similarities (semantics-only).

173 While the lexico-semantic pathway may operate on letter or open-bigram in

174 ebellar location increased neural signals of semantic prediction but did not influence episodic memor

175 llar participation in episodic memory versus semantic prediction by noninvasively stimulating with th

176 respectively support episodic memory versus semantic prediction have been associated with distinct e

177 dissociation of cerebellar contributions to semantic prediction versus episodic memory based on stim

178 ding but did not influence neural signals of semantic prediction, whereas beta stimulation of the sam

179 nitive abilities such as episodic memory and semantic prediction.

180 selectively enhanced episodic memory versus semantic prediction.

181 tory spatial attention and context-dependent semantic predictions.

182 An automatic semantic priming paradigm specifically tailored for pani

183 ns known for articulatory, phonological, and semantic processes in healthy male and female controls (

184 est that sublexical, phonological and lexico-semantic processes rely on partially distinct neural sub

185 on of labor between phonological and lexical-semantic processes.

186 default mode network plays a central role in semantic processing for abstraction of concepts.

187 Predictability is known to modulate semantic processing in language, but it is unclear to wh

188 urther evidence for the domain generality of semantic processing in the brain.

189 his effect could not be explained by lexical-semantic processing of the verbs themselves.

190 training effects within the occipitotemporal semantic processing region.

191 Studies of semantic processing show that similar neural patterns ar

192 novices from regions implicated in creative semantic processing to regions implicated in improvised

193 at deny a role for the arcuate fasciculus in semantic processing, and for ventral-stream pathways in

194 were visible in brain areas associated with semantic processing, and were differently expressed in t

195 gnitive components: phonological production, semantic processing, phonological recognition, and execu

196 disruption in somatosensory, homeostatic and semantic processing, underpinned by atrophy in a thalamo

197 strate that attention load gates unconscious semantic processing.

198 and labeling these changes according to the semantic profiles of emotion words.

199 Based on a semantic query, this tool will help users discover relev

200 issociation between phonological and lexical-semantic reading, the neuroanatomical basis for effects

201 The capturing of the semantic relatedness of biological entities is vital to

202 's performance using its ability to estimate semantic relatedness on standard evaluation datasets.

203 m "small data" (to create representations of semantic relations between words).

204 Abstract semantic relations can be induced by bootstrapping from

205 table, strong baseline system for extracting semantic relations from biomedical text.

206 ption of SemRep, an NLP system that extracts semantic relations from PubMed abstracts using linguisti

207 Both intra- and inter-sentential semantic relations in biomedical texts provide valuable

208 Semantic relations that reflect conceptual progression f

209 may contribute to the recognition of distant semantic relations that support insight solutions, altho

210 ow it connects different concepts and infers semantic relations.

211 a literature-scale knowledge graph based on semantic relations.

212 h colorless words (word-only) and words with semantic relationship but no orthographic similarities (

213 ity of known entity-to-entity associative or semantic relationships supported by database or literatu

214 ing supports the involvement of both general semantic representation and feature-based representation

215 es of the semantic system code for a general semantic representation and/or possess representational

216 regions to semantic content and the type of semantic representation coded (general or feature-based)

217 A semantic representation may be determined by specific se

218 eneralized semantic characteristics (general semantic representation).

219 ormance at tasks that required guidance from semantic representation, rather than those dependent on

220 Results uncover two principles of semantic representation: food-selective representations

221 these cells in behaving humans include that semantic representations are activated before episodic r

222 How semantic representations are manifest over the brain rem

223 ion in the human brain is quite complex, the semantic representations evoked by listening versus read

224 uneus (PC) and additionally observed general semantic representations in ventromedial prefrontal cort

225 l gyrus may coordinate control over concrete semantic representations that support mapping of print t

226 es were too insensitive to determine whether semantic representations were shared at a fine level of

227 perceptual representations can give rise to semantic representations, but not the reverse.

228 ' framework by combining the sparse and deep semantic representations.

229 ed with diffuse reactivation of higher-order semantic representations.

230 their knowledge of the world in categorical semantic representations.

231 areas, which therefore adopted a linguistic-semantic role.

232 e patterns as they viewed stimuli varying in semantic salience.

233 tor space model of semantics to characterize semantic search deficits in hippocampal amnesia.

234 This article describes Thalia, which is a semantic search engine that can recognize eight differen

235 While semantic segmentation algorithms enable image analysis a

236 The cascaded-CNN is a semantic segmentation image classifier and was trained u

237 In addition, we implemented a semantic segmentation method to identify tumor-infiltrat

238 a deep learning network optimized to perform semantic segmentation of kidneys and liver.

239 deep-convolutional neural network to perform semantic segmentation on quantitative phase maps.

240 Here, we apply semantic segmentation to protein structures as a novel s

241 We developed an automated semantic segmentation tool utilizing deep learning for r

242 a new model based on the architecture of the semantic segmentation U-Net model to precisely segment m

243 tworks (CNNs) have been successfully used in semantic segmentation-a subfield of image classification

244 e gained success in image analysis including semantic segmentation.

245 ed voxelwise encoding models to characterize semantic selectivity in each voxel and in each individua

246 and diseases through considering the disease semantic similarity (DISSS), the lncRNA function similar

247 phasizing the conceptual differences between semantic similarity analysis and approaches based on the

248 nd phenotypic relatedness by using HPO-based semantic similarity analysis for individuals with de nov

249 are displayed in a 2D plot that captures the semantic similarity between terms, with the option to cl

250 es to construct gene networks by calculating semantic similarity between the genes.

251 tion performance tested under four different semantic similarity calculation methods (Lin, Resnik, Sc

252 Semantic similarity can be used to generate statistical

253 previous findings of sensitivity to general semantic similarity in posterior middle/inferior tempora

254 rms the unified descriptor by fusing disease semantic similarity information, disease and circRNA Gau

255 pathogenicity prediction is combined with a semantic similarity measure to prioritize not only varia

256 te network, we conduct a gene ontology-based semantic similarity ranking to find suitable synergistic

257 it reflected the inherent difference in the semantic similarity structure of the predicted animate a

258 a word's speech envelope is enhanced by its semantic similarity to its sentential context.

259 These words, which share greater semantic similarity with other words, are more readily a

260 uences, miRNA functional similarity, disease semantic similarity, and known miRNA-disease association

261 Finally, we show that for semantic similarity-based clustering, the multicellular

262 faithful bidimensional representation of the semantic space directly from their multivariate activity

263 that hippocampal activity tracks distance in semantic space during recall supports the growing consen

264 Successfully navigating in physical or semantic space requires a neural representation of alloc

265 We offer an alternative perspective: semantic space theory.

266 ions between concepts of a novel audiovisual semantic space, and that it was possible to reconstruct,

267 ries implied implicit movements in the novel semantic space, and that such movements subtended specif

268 g beyond traditional models to study broader semantic spaces of emotion can enrich our understanding

269 tion) should underlie navigation of abstract semantic spaces, even if they are categorical and labele

270 a F5 neurons tested during pragmatic (PT) or semantic (ST) visuomotor tasks.

271 The semantic standardization of phenotype descriptions with

272 ecise, computable semantics, but adoption of semantic standards for representing phenotypic data has

273 ifferences in fixation frequencies along six semantic stimulus dimensions.

274 ng accessed, and the flexible recruitment of semantic stores linked to the content being accessed, pr

275 Here we tested whether nodes of the semantic system code for a general semantic representati

276 In the brain, the semantic system is thought to store concepts.

277 feature-based representations in the brain's semantic system.

278 the broad representational repertoire of our semantic system.

279 or are there input constraints (e.g., space, semantics) that lead to differentiated multiple maps acr

280 r, Cutler et al. use a vector space model of semantics to characterize semantic search deficits in hi

281 beliefs when information about the specific semantic trait is absent.

282 nd 2 node classification tasks: medical term semantic type classification, protein function predictio

283 the proposed terms include their brevity and semantic uniformity for N-degrons and C-degrons.

284 Rather than prescribing semantics used in correlational studies, it would be use

285 ia (10 with behavioural variant, 11 with the semantic variant and 10 with the non-fluent variant), 28

286 ique clinical model offered by patients with semantic variant of primary progressive aphasia (svPPA).

287 verlaps with concept: emotion recognition in semantic variant of primary progressive aphasia', by Ber

288 atic variant primary progressive aphasia and semantic variant PPA.

289 has been considered a right-sided variant of semantic variant primary progressive aphasia (svPPA).

290 rd deviation) age 64.8 (6.8) years], 12 with semantic variant primary progressive aphasia [four femal

291 o controls in the temporal regions, and both semantic variant primary progressive aphasia and behavio

292 18F-AV-1451 binding was increased in semantic variant primary progressive aphasia compared to

293 ioural variant, anterior insula and caudate; semantic variant, anterior temporal cortex; non-fluent v

294 ant frontotemporal dementia, non-fluent, and semantic variants of primary progressive aphasia (PPA),

295 studies have compared certain subtypes (e.g. semantic variants) or have focused on a specific cogniti

296 ssociated with higher scores in phonemic and semantic verbal fluency tests and with lower TMT A time.

297 used on the N400, a robust marker of lexical-semantic violation at the group level.

298 en exhibit heterogenous responses to lexical-semantic violation, implying that any application to het

299 is article, we propose an approach, based on Semantic Web technologies, to represent and compare PGx

300 sentations, which were sensitive to sentence semantics, were shared across perception and rehearsal o