ライフサイエンスコーパス: word

1 ic to phonological correspondence (irregular words).

2 200 ms, typically well before the end of the word.

3 ual representation that is compared to known words.

4 tiate subsequently remembered from forgotten words.

5 ely correlated with select memories for cued words.

6 e and further applied it to thousands of new words.

7 apes to complex image configurations such as words.

8 for pseudowords, when compared to irregular words.

9 awareness of the presence of faces or target words.

10 t 45 min after they had learned a list of 20 words.

11 and grapheme-to-phoneme processing of pseudo-words.

12 of the way that humans produce morphemes and words.

13 moted the use of gender-neutral pronouns and words.

14 ccording to the semantic profiles of emotion words.

15 readers and those with less experience with words.

16 video clips of the same actress pronouncing words.

17 ly led to an increase in cooperation-related words.

18 rds when they were preceded by panic-trigger words.

19 ng expectancy and reading of odor-associated words.

20 s: 135 words (35%) of new patient notes, 102 words (27%) of return patient notes.

21 of documentation after clinical visits: 135 words (35%) of new patient notes, 102 words (27%) of ret

22 thesis for developmental evolution: in other words, a hypothesis for how much developmental evolution

23 We hypothesized that viewing a jumbled word activates a visual representation that is compared

24 -addressable memory compares an input search word against all rows of stored words in an array in a h

25 In other words, an initial interaction with particles requiring a

26 We find that word and color incongruencies between a subliminal prime

27 MRI investigation using data from 30 diverse word and sentence comprehension experiments (481 unique

28 eft ear, eliciting slower responses when the word and the side are incongruent-the conflict effect.

29 ncephalograms [EEGs]) as people read word-by-word and then correlated the predictability in context o

30 rdings of brain responses to degraded spoken words and experimentally manipulated signal quality and

31 -functional role: lexical processing of real words and grapheme-to-phoneme processing of pseudo-words

32 atives provided higher quantities of content words and lexical diversity compared to composite pictur

33 Participants learned associations between words and locations in left or right visual fields with

34 His words and message that day have continued to resonate fo

35 passively viewed images of letters, English words and non-word strings, and tested the capacity of t

36 ion and dissociation in the ability to write words and numbers.

37 tal regions impacted the ability to retrieve words and produce them within increasingly complex combi

38 eaf readers made lexical decisions to target words and pseudowords.

39 xhibited behavioral advantages for reversing words and sentences of varying complexity, irrespective

40 outcome (Comprehensive Aphasia Test: Spoken Words and Sentences).

41 con - implicit knowledge of the existence of words and sub-word units without any associated meaning.

42 Spoken words and syllables could be decoded from single trials,

43 nteroceptive, and emotional experiences with words and their referents.

44 cess that is influenced by context, culture, words, and gestures, and it is one of the most important

45 rons believed to imprint memories, concepts, words, and other cognitive information.

46 ic regression, using 1000 features, 100 stop words, and term frequency-inverse document frequency met

47 he model, number of features, number of stop words, and the method used to create the feature set.

48 the largest performance gain for a combined word- and sentence-level input convolutional neural netw

49 age is discovering the rules that govern how words are combined in order to convey meanings.

50 on and that subsequent comparisons to stored words are consistent with activations of the visual word

51 We find that certain words are correctly retrieved across participants irresp

52 Although words are learned through language and defined by their

53 listeners recognize more isolated words when words are presented later rather than earlier in noise.

54 share greater semantic similarity with other words, are more readily available during retrieval and l

55 novel words (i.e., correctly classifying the word as novel), and preonset and postonset activity arou

56 eated words (i.e., correctly classifying the word as repeated).

57 It also leads to increased awareness of words as linguistic units and to more fine-grained phono

58 These players come alive via their own words, as complex individuals, both heroes and anti-hero

59 Mini-mental State Examination, Control Oral Word Association Test, Trail Making Test and Digit Span

60 y and consistency of their exposure to adult words (AWs) and adult-infant conversational turns (CTs).

61 are reported in international dollars using Word Bank purchasing power parity conversion factors at

62 d motif-based profile-kernel approaches with word-based (ProtVec) solutions to machine learning prote

63 ing a learning algorithm based on the bag-of-words (BoW) model, where, by learning and recognizing th

64 In other words, breakthrough sequences of vaccinees would be less

65 Critically, this effect disappeared for words but not for pseudowords, at the N250-an ERP compon

66 We can value diversity not only in words, but also in actions.

67 n language communication is via sequences of words, but language understanding requires constructing

68 cause slower responses to non-Stroop target words-but only if the task is to name the target word (l

69 ion, and then to decode this representation, word by word, into an English sentence.

70 electroencephalograms [EEGs]) as people read word-by-word and then correlated the predictability in c

71 at are common to all, allowing us to capture word-by-word signal variations, consistent across subjec

72 s have shown that linguistic markers such as word choice, utterance and sentence structures can poten

73 performance and error patterns of baboons on word classification.

74 In this article, we employ text mining and word cloud analysis techniques to address these challeng

75 ation including age and DeltaMRT of negative word-color congruent (NEG-C), was finally observed as fo

76 ed statistics and that n-grams, higher order word combinations that humans have difficulty processing

77 ed with GABA measures to predict single real word compared to pseudo-word reading performance.

78 than individuals that are smaller; in other words, competition is asymmetric.

79 ith chronic aphasia can improve their spoken word comprehension many years after stroke.

80 ining EHS was found to improve the number of words correctly identified by an average of 8.3%-points,

81 was present regardless of whether a specific word could be predicted, providing strong evidence for t

82 at classifiers trained on responses to color words could decode color from data obtained using colore

83 -level methods (e.g., Linguistic Inquiry and Word Count [LIWC] 2015 and Language Assessment by Mechan

84 ycholinguistic tools, Linguistic Inquiry and Word Count and Empath and activities of substance users

85 ed in the gravity-dominated regime; in other words, crater growth was limited by gravity not surface

86 The word "decreased" should have been "increased".

87 Words denoting natural kinds, common actions and artefac

88 uage.SIGNIFICANCE STATEMENT The meaning of a word depends on the words surrounding it.

89 N170 component and 2) task-dependent target-word detection with the P3b component, despite no awaren

90 tonset activity around the encoding of novel words did not predict memory performance for novel words

91 rmed a typicality judgment task with written words drawn from sixteen different categories.

92 In other words, DUP may be better understood as an indicator of i

93 d with pseudorandom sequences of objects and words during a functional MRI session.

94 nstraints to predict the animacy of upcoming words during sentence comprehension, and that these pred

95 hed control subjects while reading irregular words (e.g. yacht) and pseudowords (e.g. pook).

96 ers (e.g., "elevator") compared with neutral words (e.g., "bottle"), was performed during functional

97 ences to represent bacteriocins, and apply a word embedding method that accounts for amino acid order

98 d an efficient bioinformatics approach using word embedding to summarize drug information from more t

99 In study 3, we used word embeddings derived from a large corpus of online te

100 Here we use word embeddings of protein sequences to represent bacter

101 be efficiently encoded as information-dense word embeddings(11-13) (vector representations of words)

102 ddings of syntactic information to the input word embeddings.

103 In other words, Env binds to CD4 on key immune cells and transduc

104 stantial racial disparities, with an average word error rate (WER) of 0.35 for black speakers compare

105 Average word error rates across a held-out repeat set are as low

106 say, "What if everybody did that?" In other words, even if a single person's behavior is harmless, t

107 ecision, and can generate variants for multi-word expressions.

108 and six, stratified by site and severity of word finding at baseline based on CAT Naming Objects tes

109 gnificant improvement in personally relevant word finding but did not result in an improvement in con

110 patients developed language problems such as word-finding difficulties and anomia.

111 git-Symbol Substitution Test (DSST), and the Word Fluency Test (WFT).

112 Instead, SZ recruited the visual word form area (VWFA) during both stimulus encoding and

113 this hypothesis by asking whether the visual word form area (VWFA), an experience-driven region, was

114 re consistent with activations of the visual word form area (VWFA).

115 lexicality, preceding the traditional visual word form area.

116 gates posteriorly from this region to visual word form regions and to earlier visual cortex, which, w

117 Amid a broader controversy about the role of word-form prediction in comprehension, those findings we

118 idence indicating that upcoming phonological word forms-e.g., kite vs. airplane-were predicted during

119 e long-term memory representations of visual word forms.

120 ably be attributed to prediction of upcoming word forms.

121 Information regarding lexicality and word frequency propagates posteriorly from this region t

122 cess this proto-lexicon to distinguish Maori words from Maori-like nonwords.

123 The word function has many different meanings in molecular b

124 It's been over 100 years since the word `gene' is around and progressively evolving in seve

125 hat, after the statistical relations between words have been extracted, the engagement of goal-direct

126 In a new ERP paradigm, the word 'hello' was uttered either in IDS or adult-direct s

127 In other words, how will we know what to look for if we don't fir

128 did not predict memory performance for novel words (i.e., correctly classifying the word as novel), a

129 not predict memory performance for repeated words (i.e., correctly classifying the word as repeated)

130 ggest that cerebral representations encoding word identities may be more modality-specific than often

131 epresents acoustically and visually conveyed word identities.

132 tions that best reflected the sensory-driven word identity.

133 y the model simply tries to predict a masked word in a given context.

134 input search word against all rows of stored words in an array in a highly parallel manner.

135 ords over time relative to dominance-related words in both countries.

136 uage and defined by their relations to other words in dictionaries, our understanding of word meaning

137 universally perceived, then the meanings of words in different languages should closely align.

138 me measurements were characters or number of words in each note categorized by attribution source, au

139 he statistical co-occurrence of monosyllabic words in Jueju negatively correlated with speech segment

140 und them in sequences of discrete items-from words in language or notes in music to abstract concepts

141 e ability to hold and manipulate phonemes or words in mind.

142 to search for combinations of the following words in patient visit notes: "not," "non," "n't," "no,"

143 ere time-locked to visually presented target words in sentence contexts manipulating lexical/conceptu

144 We compared the recognition of noised-masked words in the presence and in the absence of adapting noi

145 e on visual search, and responses to jumbled words in word reading tasks.

146 e, we report some benefits of adaptation for word-in-noise recognition and show that (1) adaptation o

147 ures of auditory discrimination performance (words-in-noise (WIN), quick speech-in-noise (QuickSIN),

148 Search words included astigmatism, corneal astigmatism, toric I

149 hypothesis is confirmed by showing that the word-induced incongruence effect can be detected in the

150 In other words insights from previous experiences can be applied

151 ns are uniquely able to retrieve and combine words into syntactic structure to produce connected spee

152 then to decode this representation, word by word, into an English sentence.

153 stems predicts oral reading behavior of real words, irrespective of the local concentration of GABA.

154 pp's research philosophy, and, using his own words, "it is the only way to understand the complex uni

155 In this My Word, Joseph LeDoux and Hakwan Lau argue that everyday h

156 In this My Word, Joseph LeDoux describes how his four-decade career

157 In this My Word, Joseph LeDoux describes how his work as a graduate

158 Organometallic complexes: these two words jump to the mind of the chemist and are directly a

159 h, while active earlier, show sensitivity to words later.

160 f actions protected a subsequent sequence of words learned hours later from interference provided the

161 imer's Disease Delayed Recall (CERAD-DR) and Word Learning tests, and the Animal Fluency test (AF).

162 emoving as few as three of the most frequent words led to notable improvements in well-being predicti

163 assic Simon task where participants hear the word "left" or "right" in the right or left ear, eliciti

164 weets, we provide a systematic evaluation of word-level and data-driven methods for text analysis for

165 tectures that improve significantly beyond a word-level input CNN model.

166 We find that word-level methods (e.g., Linguistic Inquiry and Word Co

167 tes of failure, persuasion words, or novelty words like "remarkable" or "unexpected." We did find tha

168 all test both immediately after learning the word list and after 24 h.

169 higher false recognition in the associative word-list task both at immediate and delayed test than c

170 We used three different methods (associative word lists and two misinformation tasks using virtual re

171 trols with morphemes and sentential prosody, word lists with lexical content but no phrase structure,

172 Here we assessed memory for word-location associations across four days, testing whe

173 When participants learned a set of related word-location associations that conformed to a general p

174 s-but only if the task is to name the target word (low-load task), and not if the task is to name the

175 In other words, males and females may agree on where they are goi

176 G, subjects participated in a visual picture-word matching task.

177 ing of pitch (i.e., tone) changes that alter word meaning is left-lateralized indicating that linguis

178 words in dictionaries, our understanding of word meaning presumably draws heavily on our nonlinguist

179 By contrast, if shared word meanings are a product of shared culture, history a

180 en by more-similar cultures had more aligned word meanings.

181 entational overlap, across individual object-word memories in vmPFC the next day.

182 The word naturally conjures fears of unexpected and freakish

183 Despite not explicitly knowing many Maori words, non-Maori-speaking New Zealanders are able to acc

184 ols were recruited by physician referral and word of mouth, respectively.

185 ence indices, measures of the most important words of interest, were calculated using Anthropac by do

186 aps the best description of life is from the words of Yogi Berra: "It's tough to make predictions, es

187 In other words, of the aboveground live tree biomass in 2012, ~1.

188 fferent from male), via network referral and word-of-mouth in Cape Town, East London, and Johannesbur

189 ons, each word-selective region responded to words on both sides of fixation.

190 Most (8/11, 73%) were nonverbal or had a few words only.

191 n separate experiments with colorless words (word-only) and words with semantic relationship but no o

192 For example, a learned sequence of words or actions can follow a common rule, which determi

193 ifferent types of memory task: a sequence of words or actions that either did or did not have a commo

194 p learning applications, discrete data, e.g. words or n-grams in language, or amino acids or nucleoti

195 ve in working memory, or predicting upcoming words or structures.

196 typically been studied by focusing on either words or their constituent elements (for example, low-le

197 them into behaviorally distinct categories ('words' or 'call types').

198 sciplines, base rates of failure, persuasion words, or novelty words like "remarkable" or "unexpected

199 these universal properties-those related to word order-result from a process of optimization for eff

200 rns that predict a large subset of the major word-order correlations across languages.

201 biguity and simulate grammars with optimized word-order parameters on large-scale data from 51 langua

202 ion of grammars toward efficiency results in word-order patterns that predict a large subset of the m

203 c reasoning across languages, with different word orders having different pragmatic affordances.

204 significant improvements for trained spoken words over therapy versus standard care (11%, Cohen's d=

205 We found an increase in cooperation-related words over time relative to dominance-related words in b

206 tern classification) and declarative memory (word pair associates) across a 4-hr daytime training-ret

207 polysomnography and performed a declarative word-pair learning task.

208 by violations (e.g., syntactically incorrect words paired with incongruent completion of a chord prog

209 xercise before or after studying a series of word pairs (cloud-ivory), and completed cued-recall (clo

210 task that requires the encoding of distinct word pairs in memory.

211 , patients rated panic-trigger/panic-symptom word pairs with higher relatedness and higher negative v

212 omedial prefrontal cortex (vmPFC) for object-word pairs, and posterior hippocampus and posteromedial

213 ppocampus and posteromedial cortex for scene-word pairs.

214 or processing of panic-trigger/panic-symptom word pairs.

215 International Reading Speed Test (IReST) as words per minute (wpm).

216 the accelerated rise of cooperation-related words preceded both the English Civil War (1642) and the

217 no effect on air temperature (Table 1)" the word 'precipitation' was omitted.

218 Participants viewed pairs of words presented simultaneously.

219 Memory for specific words processed in the cued hemisphere (ipsilateral to s

220 een structural white matter connectivity and word production in a cross-sectional study of 42 partici

221 oth semantic and phonological processing for word production.

222 ure-naming test and a computational model of word production.

223 Specifically, learning a sequence of words protected a subsequent sequence of actions learned

224 In other words, rationalization often occurs antecedent to a beha

225 By contrast, single word reading (d = 0.35, p = 0.31) was intact.

226 predict single real word compared to pseudo-word reading performance.

227 A delayed word reading task was administered inside MEG to assess

228 al search, and responses to jumbled words in word reading tasks.

229 rsal spatiotemporal cluster during irregular word reading.

230 ere cued to perform three different tasks of word-reading, pattern-viewing and finger-tapping.

231 worse) T scores on GP-DH, WAIS-IIIDS, Stroop Word-Reading, TMT-A; lower motor and SIP summary T score

232 ch (WAIS-IIISS), Stroop Color-Naming, Stroop Word-Reading, Trail-Making Test-A (TMT-A), Color Trails-

233 and 5 (2011-2013) using 3 tests: the Delayed Word Recall Test (DWRT), the Digit-Symbol Substitution T

234 neural network (HSNN) optimized to maximize word recognition accuracy in noise and multiple talkers

235 solate the spatiotemporal dynamics of visual word recognition across the entire left ventral occipito

236 Spoken word recognition in context is remarkably fast and accur

237 icity, that are not directly relevant to the word recognition task.

238 temporal cortical regions involved in visual word recognition, distinct subregions harbor slightly di

239 presentations of stimuli (either objects or words) referring to different categories implied implici

240 22 social groups from positive vs. negative words (reflecting generalized attitudes) was highly corr

241 s. cold, and even competent vs. incompetent, words (reflecting specific beliefs).

242 We identified articles using key words related to pediatric critical illness and outcome

243 , only under visual deprivation, distributed word-related neural circuits 'grew into' the deprived vi

244 To date we know little about natural emotion word repertoires, and whether or how they are associated

245 atures from qualifying radiology reports: 1) word representations (n-grams) and 2) standardized clini

246 T1D had longer SRET negative word response times (P = 0.017) and higher depression ra

247 In this My Word, Rodrigo Quian Quiroga discusses neural mechanisms

248 viewed UGI reports from 1987 to 2017 using a word scanning software program to identify individuals t

249 Unlike retinotopic regions, each word-selective region responded to words on both sides o

250 in which mice distinguished between tactile "word" sequences constructed from distinct vibrations del

251 ommon to all, allowing us to capture word-by-word signal variations, consistent across subjects and a

252 hat goes beyond the prediction of individual words.SIGNIFICANCE STATEMENT Language inputs unfold very

253 ding sentences modulates brain activity in a word-specific manner across subjects.SIGNIFICANCE STATEM

254 nd in the ERP data, behavioural responses to words still benefited from the physical overlap between

255 Word stimuli (verbs, nouns) consisted of video clips of

256 Gamma power induced by word stimuli increased after LOC while its frequency pro

257 In three experiments using word stimuli, domain-relevant and atypical conceptual ac

258 wed images of letters, English words and non-word strings, and tested the capacity of those neuronal

259 We compute the gain in word success rate provided by a reservoir computing devi

260 pts, job candidates and grant applications - words such as incremental, novelty, mechanism, descripti

261 In other words, sulfide likely disrupted microbial cross-feeding

262 ATEMENT The meaning of a word depends on the words surrounding it.

263 ed more strongly (1) to lists of unconnected words than to sentences, and (2) in paradigms with an ex

264 vel auditory tracking of speech improves for words that are more related to their preceding context.

265 For each word, the patient's task was to decide whether it was no

266 In other words, the nonequilibrium flux distribution is under kin

267 In other words, the phenotype of a plant is not only the result o

268 infants just beginning to speak their first words, the way in which an object is named guides infant

269 Although not meant to be the final word, these theoretical platforms harbor potential for g

270 xts do not constrain strongly for a specific word, they do allow us to predict some upcoming informat

271 In other words, they form the pallido-cortical arm of the cortico

272 nderlying free energy landscape is: In other words, this distribution cannot be broader than the sing

273 ng requires a fast conversion of the written word to both phonological and semantic codes.

274 template database of fixed-length amino acid words to determine estimated class-membership probabilit

275 native language, learn to associate auditory words to visual objects and assemble a lexicon.

276 iers collapse to the class means or in other words, to the simplex ETF (i.e., to a self-dual configur

277 knowledge of the existence of words and sub-word units without any associated meaning.

278 Many of the words used by scientists when reviewing manuscripts, job

279 provide evidence that the meanings of common words vary in ways that reflect the culture, history and

280 novel) predicted subsequent memory (when the word was later repeated).

281 piking activity in the hippocampus (when the word was novel) predicted subsequent memory (when the wo

282 Change on ACT trained words was associated with volume of pretherapy right hem

283 nce of fillers, compared to that of ordinary words, was associated with a greater magnitude of high g

284 After we listen to a series of words, we can silently replay them in our mind.

285 teractions that allow us to identify written words, we performed direct intra-cranial recordings in a

286 The most-aligned words were from semantic domains with high internal stru

287 In this task, words were presented in a continuous series and eventual

288 s have written and to pay attention to every word when you write.

289 aster lexical decisions to the panic-symptom words when they were preceded by panic-trigger words.

290 fact that listeners recognize more isolated words when words are presented later rather than earlier

291 On the other hand, pseudo-words, which require grapheme-to-phoneme conversion, are

292 These words, which share greater semantic similarity with othe

293 tioned the...," we can predict that the next word will be animate rather than inanimate (we can cauti

294 mpairments including difficulties in reading words with exceptional orthographic to phonological corr

295 riments with colorless words (word-only) and words with semantic relationship but no orthographic sim

296 Words with strong olfactory associations (OW) (e.g. "ban

297 embeddings(11-13) (vector representations of words) without human labelling or supervision.

298 menon in separate experiments with colorless words (word-only) and words with semantic relationship b

299 is opening up new opportunities to decipher words written millennia ago, as part of our Cultural Her

300 tructure of language: the generic use of the word "you" (e.g., "You can't understand someone until yo