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Conceptual blending is closely related to frame-based theories, but goes beyond these primarily in that it is a theory of how to combine frames (or frame-like objects). An early computational model of a process called "view application", which is closely related to conceptual blending (which did not exist at the time), was implemented in the 1980s by Shrager at Carnegie Mellon University and ...
BERT is trained by masked token prediction and next sentence prediction. As a result of this training process, BERT learns contextual, latent representations of tokens in their context, similar to ELMo and GPT-2. [4] It found applications for many natural language processing tasks, such as coreference resolution and polysemy resolution. [5]
Skip-Thought trains an encoder-decoder structure for the task of neighboring sentences predictions; this has been shown to achieve worse performance than approaches such as InferSent or SBERT. An alternative direction is to aggregate word embeddings, such as those returned by Word2vec, into sentence embeddings.
A form of prediction is also thought to occur in some types of lexical priming, a phenomenon whereby a word becomes easier to process if it is preceded by a related word. [1] Linguistic prediction is an active area of research in psycholinguistics and cognitive neuroscience.
Blended artificial intelligence (blended AI) refers to the blending of different artificial intelligence techniques or approaches to achieve more robust and practical solutions. It involves integrating multiple AI models, algorithms , and technologies to leverage their respective strengths and compensate for their weaknesses.
The child must receive a blended sentence if any of the following are true: This is the child’s second such offense, or if the local district attorney requests it, or if the child is 14 or 15 ...
Blended sentencing offers an opportunity for many serious youthful offenders who would otherwise be trapped in a cycle of gun and gang violence to break out of that cycle and become productive ...
By contrast, generative theories generally provide performance-based explanations for the oddness of center embedding sentences like one in (2). According to such explanations, the grammar of English could in principle generate such sentences, but doing so in practice is so taxing on working memory that the sentence ends up being unparsable ...