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Document AI combines text data, which has a time dimension, with other types of data, such as the position of an address in a business letter, which is spatial. Historically in machine learning spatial data was analyzed using a convolutional neural network , and temporal data using a recurrent neural network .
AI applications analyze media content such as movies, TV programs, advertisement videos or user-generated content. The solutions often involve computer vision. Typical scenarios include the analysis of images using object recognition or face recognition techniques, or the analysis of video for scene recognizing scenes, objects or faces.
AI in education; AI-assisted reverse engineering; AI-assisted targeting in the Gaza Strip; AI: When a Robot Writes a Play; Aidoc; Aisera; AIVA; Alice (Microsoft) Anyword; Apple Intelligence; Artificial imagination; Outline of artificial intelligence; Artificial Intelligence Applications Institute; Artificial intelligence in architecture ...
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 automatic analysis of vast textual corpora has created the possibility for scholars to analyze millions of documents in multiple languages with very limited manual intervention. Key enabling technologies have been parsing, machine translation , topic categorization , and machine learning.
Machine learning, statistical analysis and/or natural language processing are often used in IE. IE on non-text documents is becoming an increasingly interesting topic [when?] in research, and information extracted from multimedia documents can now [when?] be expressed in a high level structure as it is done on text. This naturally leads to the ...
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