The present study examined the general hypothesis that, as for nouns, stable representations of semantic knowledge relative to situations expressed by verbs are available and accessible in long term memory in normal people. Regular associations between verbs and past tenses in French adults allowed to abstract two superordinate semantic features in the representation of verb meaning: durativity and resultativity. A pilot study was designed to select appropriate items according to these features: durative, non-resultative verbs and non-durative, resultative verbs. An experimental study was then conducted to assess semantic priming in French adults with two visual semantic-decision tasks at a 200- and 100-ms SOA. In the durativity decision task, participants had to decide if the target referred to a durable or non-durable situation. In the resultativity decision task, they had to decide if it referred to a situation with a directly observable outcome or without any clear external outcome. Targets were preceded by similar, opposite, and neutral primes. Results showed that semantic priming can tap verb meaning at a 200- and 100-ms SOA, with the restriction that only the positive value of each feature benefited from priming, that is the durative and resultative values. Moreover, processing of durativity and resultativity is far from comparable since facilitation was shown on the former with similar and opposite priming, whereas it was shown on the latter only with similar priming. Overall, these findings support Le Ny's (in: Saint-Dizier, Viegas (eds) Computational lexical semantics, 1995; Cahier de Recherche Linguistique LanDisCo 12:85-100, 1998; Comment l'esprit produit du sens, 2005) general hypothesis that classificatory properties of verbs could be interpreted as semantic features and the view that semantic priming can tap verb meaning, as noun meaning.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1007/s10936-007-9066-7 | DOI Listing |
Med Image Anal
January 2025
General Hospital of the Southern Theatre Command, PLA, Guangzhou, China; The First School of Clinical Medicine, Southern Medical University, Guangzhou, China. Electronic address:
Diagnostic cardiologists have considerable clinical demand for precise segmentation of echocardiography to diagnose cardiovascular disease. The paradox is that manual segmentation of echocardiography is a time-consuming and operator-dependent task. Computer-aided segmentation can reduce the workflow greatly.
View Article and Find Full Text PDFDiabetes Metab Res Rev
January 2025
Rush Alzheimer's Disease Centre, Rush University Medical Center, Chicago, Illinois, USA.
Diabetes increases the risk of dementia, and insulin resistance (IR) has emerged as a potential unifying feature. Here, we review published findings over the past 2 decades on the relation of diabetes and IR to brain health, including those related to cognition and neuropathology, in the Religious Orders Study, the Rush Memory and Aging Project, and the Minority Aging Research Study (ROS/MAP/MARS), three harmonised cohort studies of ageing and dementia at the Rush Alzheimer's Disease Center (RADC). A wide range of participant data, including information on medical conditions such as diabetes and neuropsychological tests, as well as other clinical and laboratory-based data collected annually.
View Article and Find Full Text PDFFront Plant Sci
January 2025
College of Big Data, Yunnan Agricultural University, Kunming, China.
Introduction: Weeds are a major factor affecting crop yield and quality. Accurate identification and localization of crops and weeds are essential for achieving automated weed management in precision agriculture, especially given the challenges in recognition accuracy and real-time processing in complex field environments. To address this issue, this paper proposes an efficient crop-weed segmentation model based on an improved UNet architecture and attention mechanisms to enhance both recognition accuracy and processing speed.
View Article and Find Full Text PDFBehav Res Methods
January 2025
CogNosco Lab, Department of Psychology and Cognitive Sciences, University of Trento, Trento, Italy.
We introduce EmoAtlas, a computational library/framework extracting emotions and syntactic/semantic word associations from texts. EmoAtlas combines interpretable artificial intelligence (AI) for syntactic parsing in 18 languages and psychologically validated lexicons for detecting the eight emotions in Plutchik's theory. We show that EmoAtlas can match or surpass transformer-based natural language processing techniques, BERT or large language models like ChatGPT 3.
View Article and Find Full Text PDFSci Rep
January 2025
College of Mathematics and Computer Science, Guangdong Ocean University, Zhanjiang, 524088, China.
To address the problems of complex cloud features in satellite cloud maps, inaccurate typhoon localization, and poor target detection accuracy, this paper proposes a new typhoon localization algorithm, named TGE-YOLO. It is based on the YOLOv8n model with excellent high-low feature fusion capability and innovatively achieves the organic combination of feature fusion, computational efficiency, and localization accuracy. Firstly, the TFAM_Concat module is creatively designed in the neck network, which comprehensively utilizes the detailed information of shallow features and the semantic information of deeper features, enhancing the fusion ability of features at each layer.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!