Semantics without semantic content Harris, Daniel W.
Mind & language,
June 2022, 2022-06-00, 20220601, Letnik:
37, Številka:
3
Journal Article
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I argue that semantics is the study of the proprietary database of a centrally inaccessible and informationally encapsulated input–output system. This system's role is to encode and decode partial ...and defeasible evidence of what speakers are saying. Since information about nonlinguistic context is therefore outside the purview of semantic processing, a sentence's semantic value is not its content but a partial and defeasible constraint on what it can be used to say. I show how to translate this thesis into a detailed compositional‐semantic theory based on the influential framework of Heim and Kratzer. This approach situates semantics within an independently motivated account of human cognitive architecture and reveals the semantics–pragmatics interface to be grounded in the underlying interface between modular and central systems.
This letter presents a novel supervised change detection method based on a deep siamese semantic network framework, which is trained by using improved triplet loss function for optical aerial images. ...The proposed framework can not only extract features directly from image pairs which include multiscale information and are more abstract as well as robust, but also enhance the interclass separability and the intraclass inseparability by learning semantic relation. The feature vectors of the pixels pair with the same label are closer, and at the same time, the feature vectors of the pixels with different labels are farther from each other. Moreover, we use the distance of the feature map to detect the changes on the difference map between the image pair. Binarized change map can be obtained by a simple threshold. Experiments on optical aerial image data set validate that the proposed approach produces comparable, even better results, favorably to the state-of-the-art methods in terms of F-measure.
Older adults tend to have a broader vocabulary compared to younger adults – indicating a richer storage of semantic knowledge – but their retrieval abilities decline with age. Recent advances in ...quantitative methods based on network science have investigated the effect of aging on semantic memory structure. However, it is yet to be determined how this aging effect on semantic memory structure relates to its overall flexibility. Percolation analysis provides a quantitative measure of the flexibility of a semantic network, by examining how a semantic memory network is resistant to “attacks” or breaking apart. In this study, we incorporated percolation analyses to examine how semantic networks of younger and older adults break apart to investigate potential age-related differences in language production. We applied the percolation analysis to 3 independent sets of data (total N = 78 younger, 78 older adults) from which we generated semantic networks based on verbal fluency performance. Across all 3 datasets, the percolation integrals of the younger adults were larger than older adults, indicating that older adults' semantic networks were less flexible and broke down faster than the younger adults'. Our findings provide quantitative evidence for diminished flexibility in older adults' semantic networks, despite the stability of semantic knowledge across the lifespan. This may be one contributing factor to age-related differences in language production.
Conceptual knowledge reflects our multi-modal ‘semantic database’. As such, it brings meaning to all verbal and non-verbal stimuli, is the foundation for verbal and non-verbal expression and provides ...the basis for computing appropriate semantic generalizations. Multiple disciplines (e.g. philosophy, cognitive science, cognitive neuroscience and behavioural neurology) have striven to answer the questions of how concepts are formed, how they are represented in the brain and how they break down differentially in various neurological patient groups. A long-standing and prominent hypothesis is that concepts are distilled from our multi-modal verbal and non-verbal experience such that sensation in one modality (e.g. the smell of an apple) not only activates the intramodality long-term knowledge, but also reactivates the relevant intermodality information about that item (i.e. all the things you know about and can do with an apple). This multi-modal view of conceptualization fits with contemporary functional neuroimaging studies that observe systematic variation of activation across different modality-specific association regions dependent on the conceptual category or type of information. A second vein of interdisciplinary work argues, however, that even a smorgasbord of multi-modal features is insufficient to build coherent, generalizable concepts. Instead, an additional process or intermediate representation is required. Recent multidisciplinary work, which combines neuropsychology, neuroscience and computational models, offers evidence that conceptualization follows from a combination of modality-specific sources of information plus a transmodal ‘hub’ representational system that is supported primarily by regions within the anterior temporal lobe, bilaterally.
•Summarize deep learning methods for semantic segmentation of remote sensing images.•Identify three major challenges faced by researchers.•Summarize the innovative development to address them.
...Semantic segmentation of remote sensing imagery has been employed in many applications and is a key research topic for decades. With the success of deep learning methods in the field of computer vision, researchers have made a great effort to transfer their superior performance to the field of remote sensing image analysis. This paper starts with a summary of the fundamental deep neural network architectures and reviews the most recent developments of deep learning methods for semantic segmentation of remote sensing imagery including non-conventional data such as hyperspectral images and point clouds. In our review of the literature, we identified three major challenges faced by researchers and summarize the innovative development to address them. As tremendous efforts have been devoted to advancing pixel-level accuracy, the emerged deep learning methods demonstrated much-improved performance on several public data sets. As to handling the non-conventional, unstructured point cloud and rich spectral imagery, the performance of the state-of-the-art methods is, on average, inferior to that of the satellite imagery. Such a performance gap also exists in learning from small data sets. In particular, the limited non-conventional remote sensing data sets with labels is an obstacle to developing and evaluating new deep learning methods.
Human thought and language rely on the brain's ability to combine conceptual information. This fundamental process supports the construction of complex concepts from basic constituents. For example, ...both "jacket" and "plaid" can be represented as individual concepts, but they can also be integrated to form the more complex representation "plaid jacket." Although this process is central to the expression and comprehension of language, little is known about its neural basis. Here we present evidence for a neuroanatomic model of conceptual combination from three experiments. We predicted that the highly integrative region of heteromodal association cortex in the angular gyrus would be critical for conceptual combination, given its anatomic connectivity and its strong association with semantic memory in functional neuroimaging studies. Consistent with this hypothesis, we found that the process of combining concepts to form meaningful representations specifically modulates neural activity in the angular gyrus of healthy adults, independent of the modality of the semantic content integrated. We also found that individual differences in the structure of the angular gyrus in healthy adults are related to variability in behavioral performance on the conceptual combination task. Finally, in a group of patients with neurodegenerative disease, we found that the degree of atrophy in the angular gyrus is specifically related to impaired performance on combinatorial processing. These converging anatomic findings are consistent with a critical role for the angular gyrus in conceptual combination.
Recent mask proposal models have significantly improved the performance of open-vocabulary semantic segmentation. However, the use of a 'background' embedding during training in these methods is ...problematic as the resulting model tends to over-learn and assign all unseen classes as the background class instead of their correct labels. Furthermore, they ignore the semantic relationship of text embeddings, which arguably can be highly informative for open-vocabulary prediction as some classes may have close relationship with other classes. To this end, this paper proposes novel class enhancement losses to bypass the use of the 'background' embbedding during training, and simultaneously exploit the semantic relationship between text embeddings and mask proposals by ranking the similarity scores. To further capture the relationship between base and novel classes, we propose an effective pseudo label generation pipeline using the pretrained vision-language model. Extensive experiments on several benchmark datasets show that our method achieves overall the best performance for open-vocabulary semantic segmentation. Our method is flexible, and can also be applied to the zero-shot semantic segmentation problem.
Semantic Communication (SC) has emerged as a novel communication paradigm that provides a receiver with meaningful information extracted from the source to maximize information transmission ...throughput in wireless networks, beyond the theoretical capacity limit. Despite the extensive research on SC, there is a lack of comprehensive survey on technologies, solutions, applications, and challenges for SC. In this article, the development of SC is first reviewed and its characteristics, architecture, and advantages are summarized. Next, key technologies such as semantic extraction, semantic encoding, and semantic segmentation are discussed and their corresponding solutions in terms of efficiency, robustness, adaptability, and reliability are summarized. Applications of SC to UAV communication, remote image sensing and fusion, intelligent transportation, and healthcare are also presented and their strategies are summarized. Finally, some challenges and future research directions are presented to provide guidance for further research of SC.
Semantic cognition requires conceptual representations shaped by verbal and nonverbal experience and executive control processes that regulate activation of knowledge to meet current situational ...demands. A complete model must also account for the representation of concrete and abstract words, of taxonomic and associative relationships, and for the role of context in shaping meaning. We present the first major attempt to assimilate all of these elements within a unified, implemented computational framework. Our model combines a hub-and-spoke architecture with a buffer that allows its state to be influenced by prior context. This hybrid structure integrates the view, from cognitive neuroscience, that concepts are grounded in sensory-motor representation with the view, from computational linguistics, that knowledge is shaped by patterns of lexical co-occurrence. The model successfully codes knowledge for abstract and concrete words, associative and taxonomic relationships, and the multiple meanings of homonyms, within a single representational space. Knowledge of abstract words is acquired through (a) their patterns of co-occurrence with other words and (b) acquired embodiment, whereby they become indirectly associated with the perceptual features of co-occurring concrete words. The model accounts for executive influences on semantics by including a controlled retrieval mechanism that provides top-down input to amplify weak semantic relationships. The representational and control elements of the model can be damaged independently, and the consequences of such damage closely replicate effects seen in neuropsychological patients with loss of semantic representation versus control processes. Thus, the model provides a wide-ranging and neurally plausible account of normal and impaired semantic cognition.
One speech sound can be associated with multiple meanings through iconicity, indexicality, and/or systematicity. It was not until recently that this "pluripotentiality" of sound symbolism attracted ...serious attention, and it remains uninvestigated how pluripotentiality may arise. In the current study, Japanese, Korean, Mandarin, and English speakers rated unfamiliar jewel names on three semantic scales: size, brightness, and hardness. The results showed language-specific and cross-linguistically shared pluripotential sound symbolism. Japanese speakers associated voiced stops with large and dark jewels, whereas Mandarin speakers associated i with small and bright jewels. Japanese, Mandarin, and English speakers also associated lip rounding with darkness and softness. These sound-symbolic meanings are unlikely to be obtained through metaphorical or metonymical extension, nor are they reported to colexify. Notably, in a purely semantic network without the mediation of lip rounding, softness can instead be associated with brightness, as illustrated by synesthetic metaphors such as yawaraka-na hizashi /jawaɾakanaçizaɕi/ "a gentle (lit. soft) sunshine" in Japanese. These findings suggest that the semantic networks of sound symbolism may not coincide with those of metaphor or metonymy. The current study summarizes the findings in the form of (phono)semantic maps to facilitate cross-linguistic comparisons of pluripotential sound symbolism.
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