Difference between revisions of "Applications of Neural Networks to Semantic Understanding"
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Semantic processing in the brain involves a network of interconnected brain regions that work together to interpret language data and assign semantic meaning. While definite consensus on which regions of the brain are responsible for semantic processing and what their exact mechanism is has not yet been reached, neuroimaging studies have indicated that a distinct set of 7 regions is reliably activated during semantic processing[https://www.sciencedirect.com/science/article/pii/S1364661322003230#s0005]. These include the posterior inferior parietal lobe, middle temporal gyrus, fusiform and parahippocampal gyri, dorsomedial prefrontal cortex, inferior frontal gyrus, ventromedial prefrontal cortex, and posterior cingulate gyrus[https://www.sciencedirect.com/science/article/pii/S1364661322003230#s0005]. | Semantic processing in the brain involves a network of interconnected brain regions that work together to interpret language data and assign semantic meaning. While definite consensus on which regions of the brain are responsible for semantic processing and what their exact mechanism is has not yet been reached, neuroimaging studies have indicated that a distinct set of 7 regions is reliably activated during semantic processing[https://www.sciencedirect.com/science/article/pii/S1364661322003230#s0005]. These include the posterior inferior parietal lobe, middle temporal gyrus, fusiform and parahippocampal gyri, dorsomedial prefrontal cortex, inferior frontal gyrus, ventromedial prefrontal cortex, and posterior cingulate gyrus[https://www.sciencedirect.com/science/article/pii/S1364661322003230#s0005]. | ||
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Computational hypotheses about how semantic information is encoded can be grouped into three model types, category-based, feature-based, and vector space representations [https://www.sciencedirect.com/science/article/pii/S1364661322003230#s0005]. In the first type of model, semantic concepts are processed as numerous discrete categories that correspond to a given input— terms with similar concepts are associated together and activate the same regions [https://www.sciencedirect.com/science/article/pii/S1364661322003230#s0005]. The second posits that semantic information is processed as a number of different features, with each perceived component being linked to its associated features— similar concepts are associated with each other based on common properties [https://www.sciencedirect.com/science/article/pii/S1364661322003230#s0005]. These two approaches allow semantic data to be thought of as vectors existing in high dimensional space, with the category-based model encoding data as belonging to distinct categories and the feature-based model encoding specific terms as vectors with high values in the features they are associated with[https://www.sciencedirect.com/science/article/pii/S1364661322003230#s0005]. The last of these proposals models semantic information as also existing in a high dimensional space, but without any interpretable meaning for its corresponding dimensions [https://www.sciencedirect.com/science/article/pii/S1364661322003230#s0005]. Concepts can be understood as similar to each other based on where their vectors are located in the model[https://www.sciencedirect.com/science/article/pii/S1364661322003230#s0005]. | Computational hypotheses about how semantic information is encoded can be grouped into three model types, category-based, feature-based, and vector space representations [https://www.sciencedirect.com/science/article/pii/S1364661322003230#s0005]. In the first type of model, semantic concepts are processed as numerous discrete categories that correspond to a given input— terms with similar concepts are associated together and activate the same regions [https://www.sciencedirect.com/science/article/pii/S1364661322003230#s0005]. The second posits that semantic information is processed as a number of different features, with each perceived component being linked to its associated features— similar concepts are associated with each other based on common properties [https://www.sciencedirect.com/science/article/pii/S1364661322003230#s0005]. These two approaches allow semantic data to be thought of as vectors existing in high dimensional space, with the category-based model encoding data as belonging to distinct categories and the feature-based model encoding specific terms as vectors with high values in the features they are associated with[https://www.sciencedirect.com/science/article/pii/S1364661322003230#s0005]. The last of these proposals models semantic information as also existing in a high dimensional space, but without any interpretable meaning for its corresponding dimensions [https://www.sciencedirect.com/science/article/pii/S1364661322003230#s0005]. Concepts can be understood as similar to each other based on where their vectors are located in the model[https://www.sciencedirect.com/science/article/pii/S1364661322003230#s0005]. | ||
Revision as of 07:27, 22 October 2022
In the context of artificial neural networks, semantics refers to the capabilities of networks to understand and represent the meaning of information, specifically how the meaning of words and sentences are discerned. The advance of neural networks in recent decades has had a profound impact on both neuroscience and philosophy dealing with semantic processing and understanding.
In the brain, semantics are processed by networks of neurons in several regions of the brain. The specific neural underpinnings of semantic understanding is a currently evolving area of research, with a variety of hypotheses on the exact mechanisms for semantic processing in the brain being explored at this time. Nevertheless, the brain’s ability to continually learn and refine its semantic understanding has provided an important model for advancements in artificial systems attempting to emulate these features.
In artificial systems, neural networks analyze linguistic data— identifying patterns to realize context and relationships between such data to create representations of meaning. A variety of model types are popular for attempting to encode semantics, including convolutional neural networks (CNN), recurrent neural networks (RNN), and transformer models.
Contents
- 1 Mechanisms for semantic representation in the brain
- 2 Representations of semantics in artificial neural networks
- 3 Comparison of biological and artificial systems
- 4 Applications of biological mechanisms to artificial neural networks
- 5 Philosophical implications of artificial models of semantic representation
Mechanisms for semantic representation in the brain
Semantic processing in the brain involves a network of interconnected brain regions that work together to interpret language data and assign semantic meaning. While definite consensus on which regions of the brain are responsible for semantic processing and what their exact mechanism is has not yet been reached, neuroimaging studies have indicated that a distinct set of 7 regions is reliably activated during semantic processing[1]. These include the posterior inferior parietal lobe, middle temporal gyrus, fusiform and parahippocampal gyri, dorsomedial prefrontal cortex, inferior frontal gyrus, ventromedial prefrontal cortex, and posterior cingulate gyrus[2].
File:Https://ars.els-cdn.com/content/image/1-s2.0-S1364661322003230-gr1.jpg
Computational hypotheses about how semantic information is encoded can be grouped into three model types, category-based, feature-based, and vector space representations [3]. In the first type of model, semantic concepts are processed as numerous discrete categories that correspond to a given input— terms with similar concepts are associated together and activate the same regions [4]. The second posits that semantic information is processed as a number of different features, with each perceived component being linked to its associated features— similar concepts are associated with each other based on common properties [5]. These two approaches allow semantic data to be thought of as vectors existing in high dimensional space, with the category-based model encoding data as belonging to distinct categories and the feature-based model encoding specific terms as vectors with high values in the features they are associated with[6]. The last of these proposals models semantic information as also existing in a high dimensional space, but without any interpretable meaning for its corresponding dimensions [7]. Concepts can be understood as similar to each other based on where their vectors are located in the model[8].
