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Content-based classification is classification in which the weight given to particular subjects in a document determines the class to which the document is assigned. It is, for example, a common rule for classification in libraries, that at least 20% of the content of a book should be about the class to which the book is assigned. [1]
Additionally, for the specific purpose of classification, supervised alternatives have been developed to account for the class label of a document. [4] Lastly, binary (presence/absence or 1/0) weighting is used in place of frequencies for some problems (e.g., this option is implemented in the WEKA machine learning software system).
Document AI, also known as Document Intelligence, refers to a field of technology that employs machine learning (ML) techniques, such as natural language processing (NLP). [1] These techniques are used to develop computer models capable of analyzing documents in a manner akin to human review.
Automatic taxonomy construction (ATC) is the use of software programs to generate taxonomical classifications from a body of texts called a corpus.ATC is a branch of natural language processing, which in turn is a branch of artificial intelligence.
In machine learning, a linear classifier makes a classification decision for each object based on a linear combination of its features.Such classifiers work well for practical problems such as document classification, and more generally for problems with many variables (), reaching accuracy levels comparable to non-linear classifiers while taking less time to train and use.
In computer vision, the bag-of-words model (BoW model) sometimes called bag-of-visual-words model [1] [2] can be applied to image classification or retrieval, by treating image features as words. In document classification , a bag of words is a sparse vector of occurrence counts of words; that is, a sparse histogram over the vocabulary.
The relationship of "Semantic data models" with "physical data stores" and "real world". [1] A semantic data model (SDM) is a high-level semantics-based database description and structuring formalism (database model) for databases. This database model is designed to capture more of the meaning of an application environment than is possible with ...
Document clustering (or text clustering) is the application of cluster analysis to textual documents. It has applications in automatic document organization, topic extraction and fast information retrieval or filtering.