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Consequently, any mapping from real world applications into computer applications requires a certain amount of technical background knowledge by the user, where the semantic gap manifests itself. It is a fundamental task of software engineering to close the gap between application specific knowledge and technically doable formalization. For ...
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.
The symbol grounding problem is a concept in the fields of artificial intelligence, cognitive science, philosophy of mind, and semantics.It addresses the challenge of connecting symbols, such as words or abstract representations, to the real-world objects or concepts they refer to.
Semantic associations allow for faster object recognition. When an object has previously been associated with some sort of semantic meaning, people are more prone to correctly identify the object. Research has shown that semantic associations allow for a much quicker recognition of an object, even when the object is being viewed at varying angles.
Multimedia information retrieval (MMIR or MIR) is a research discipline of computer science that aims at extracting semantic information from multimedia data sources. [1] [failed verification] Data sources include directly perceivable media such as audio, image and video, indirectly perceivable sources such as text, semantic descriptions, [2] biosignals as well as not perceivable sources such ...
Semantics describes the processes a computer follows when executing a program in that specific language. This can be done by describing the relationship between the input and output of a program, or giving an explanation of how the program will be executed on a certain platform , thereby creating a model of computation .
A simple example is fitting a line in two dimensions to a set of observations. Assuming that this set contains both inliers, i.e., points which approximately can be fitted to a line, and outliers, points which cannot be fitted to this line, a simple least squares method for line fitting will generally produce a line with a bad fit to the data including inliers and outliers.
Semantic parsing maps text to formal meaning representations. This contrasts with semantic role labeling and other forms of shallow semantic processing, which do not aim to produce complete formal meanings. [9] In computer vision, semantic parsing is a process of segmentation for 3D objects. [10] [11] Major levels of linguistic structure