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Semantic segmentation is an approach detecting, for every pixel, the belonging class. [18] For example, in a figure with many people, all the pixels belonging to persons will have the same class id and the pixels in the background will be classified as background.
Text segmentation is the process of dividing written text into meaningful units, such as words, sentences, or topics. The term applies both to mental processes used by humans when reading text, and to artificial processes implemented in computers, which are the subject of natural language processing .
Speech segmentation is the process of identifying the boundaries between words, syllables, or phonemes in spoken natural languages.The term applies both to the mental processes used by humans, and to artificial processes of natural language processing.
U-Net was created by Olaf Ronneberger, Philipp Fischer, Thomas Brox in 2015 and reported in the paper "U-Net: Convolutional Networks for Biomedical Image Segmentation". [1] It is an improvement and development of FCN: Evan Shelhamer, Jonathan Long, Trevor Darrell (2014). "Fully convolutional networks for semantic segmentation". [2]
In machine learning, semantic analysis of a text corpus is the task of building structures that approximate concepts from a large set of documents. It generally does not involve prior semantic understanding of the documents. Semantic analysis strategies include: Metalanguages based on first-order logic, which can analyze the speech of humans.
A semantic network is used when one has knowledge that is best understood as a set of concepts that are related to one another. Most semantic networks are cognitively based. They consist of arcs (spokes) and nodes (hubs) which can be organized into a taxonomic hierarchy. Different semantic networks can also be connected by bridge nodes.
A semantic treebank is a collection of natural language sentences annotated with a meaning representation. These resources use a formal representation of each sentence's semantic structure . Semantic treebanks vary in the depth of their semantic representation.
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