View source for Applications of Neural Networks to Semantic Understanding

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.

===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[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].

===Representations of semantics in artificial neural networks===
Three of the most popular artificial architectures for understanding semantic information are convolutional neural networks, recurrent neural networks, and transformer models. CNNs process semantic information by applying convolutional filters over semantic data to capture patterns and features of the data, deriving an understanding of semantic relationships in text [https://www.diva-portal.org/smash/get/diva2:1252494/FULLTEXT01.pdf]. RNNs are commonly used to handle sequential data like what is seen with semantic processing. One type of RNN commonly used for semantic processing is Long short-term memory models— these add a mechanism of gates on each cell that allows the model to retain information longer and better understand semantic information over a larger context[https://aws.amazon.com/what-is/recurrent-neural-network/]. Transformer models, introduced in 2017, have become the standard architecture for processing semantic information. These models employ attention mechanisms that allow the model to weigh the relevance of one word or part of a set of semantic information relative to each other. Text is converted to numerical tokens by these models, and then weighted using the attention mechanism to consider the whole semantic context of given information, improving understanding of the relationship between terms. 

Semantic encoding in artificial neural networks relies on different techniques to represent and help process semantic meaning. Embedding techniques like word2vec and GloVe are used by models to create vector relationships where words with similar meanings are mapped closer together, allowing for understanding of semantic meaning in situations like similes or analogies [https://code.google.com/archive/p/word2vec/][https://wandb.ai/authors/embeddings-2/reports/An-Introduction-to-the-Global-Vectors-GloVe-Algorithm--VmlldzozNDg2NTQ]. For more complex semantic understanding, 

Two important implementations of transformer architecture are GPTs and BERT.  These frameworks allow for more robust representation of semantic meaning by introducing techniques that allow for contextual embedding— this enables transformer models to represent semantic meaning in a more nuanced fashion [https://ai.stanford.edu/blog/contextual/]. BERT (Bidirectional Encoder Representations from Transformers) was developed in 2018 by Google to provide solutions to common language tasks— this sort of transformer model uses a large amount of data to pertain the model on a variety of tasks, allowing it to understand context better. It uses a bidirectional approach to consider both the words left and right in a given set of text, allowing for a more nuanced understanding of semantic meaning[https://huggingface.co/blog/bert-101#2-how-does-bert-work]. GPT (Generative Pretrained Transformer) was developed by OpenAI on a similar architecture. GPT processes semantic data in one direction, using a causal approach to predict the next word in a sequence[https://huggingface.co/docs/transformers/en/model_doc/openai-gpt]. 

Despite impressive results from artificial models, there still exist notable limitations and challenges for modeling semantics. Perhaps the most important being the question of what sort of understanding is being generated by artificial networks— while contemporary language models are becoming increasingly adept at representing semantics mathematically and dealing with complex semantic contexts, it is still unclear as to what sort of relationship this has to human semantic understanding. The methods of semantic processing in the brain rely on experience for inputs, which come in a much more complex, multimodal form. The development of multimodal language models may provide a closer link to what is seen in biological systems, but even so true semantic understanding from artificial systems may yet remain elusive.

===Comparison of biological and artificial systems===

===Applications of biological mechanisms to artificial neural networks===

===Philosophical implications of artificial models of semantic representation===