While previous sentiment analysis research has concentrated on the interpretation of explicitly stated opinions and attitudes, this work initiates the computational study of a type of opinion implicature (i.e., opinion-oriented inference) in text. This paper described a rule-based framework for representing and analyzing opinion implicatures which we hope will contribute to deeper automatic int...

Traditional automatic pavement distress detection methods using convolutional neural networks (CNNs) require a great deal of time and resources for computing are poor in terms interpretability. Therefore, inspired by the successful application Transformer architecture natural language processing (NLP) tasks, novel method called LeViT was introduced asphalt image classification. consists layers,...

Automatic metaphor identification and interpretation in text have been traditionally considered as two separate tasks in natural language processing (NLP) and addressed individually within computational frameworks. However, cognitive evidence suggests that humans are likely to perform these two tasks simultaneously, as part of a holistic metaphor comprehension process. We present a novel method...

Because of the large number of cells to be analyzed in cases of overexposure to ionizing radiation, an automated imaging system is desirable for scoring both translocations and dicentrics. This system should include three essential steps: automatic metaphase finding, automatic image capture at high magnification, and, finally, optimized data analysis for aberration interpretation. We evaluated ...

Children generally behave more egocentrically than adults when assessing another s perspective. We argue that this difference does not, however, indicate that adults process information less egocentrically than children, but rather that adults are better able to subsequently correct an initial egocentric interpretation. An experiment tracking participants eye movements during a referential comm...

Of all tasks in photogrammetry the extraction of cartographic features is the most time consuming. Since the introduction of digital photogrammetry much attention therefore has been paid to the development of tools for a more efficient acquisition of cartographic features. Fully automatic acquisition of features like roads and buildings, however, appears to be very difficult and may even be imp...

In this paper, we present a new approach combining topological unsupervised learning with ontology based reasoning to achieve both : (i) automatic interpretation of clustering, and (ii) scaling ontology reasoning over large datasets. The interest of such approach holds on the use of expert knowledge to automate cluster labeling and gives them high level semantics that meets the user interest. T...