Titlebook: Automatic Indexing and Abstracting of Document Texts; Marie-Francine Moens Book 2002 Springer Science+Business Media New York 2002 algorit

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Automatic Indexing: The Selection of Natural Language Index Termsrds. In some cases the methods bear upon linguistic knowledge regarding a ., i.e., bearing upon the vocabulary, syntax, and semantics of the individual sentences, clauses, and phrases. The linguistic knowledge is involved in stemming procedures, phrase recognition, and phrase normalization. The exis

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Automatic Indexing: The Assignment of Controlled Language Index Termsply the term concepts is needed. This knowledge is implemented in thesauri and knowledge bases for text categorization. Building thesauri automatically remains a very difficult task. Learning the classification patterns of broad text classes is somewhat easier. Constructing a text classifier that ge

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Text Structuring and Categorization When Summarizing Legal Casesxt category, and of relevant and insignificant text components is an important first step when intellectually abstracting. Automating this process was especially useful for controlling the overload of present and future court decisions..Knowledge of the discourse structures of criminal cases proved

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The Assignment of Subject Descriptors to Magazine Articlesation situations the number of patterns is large to manually acquire. In this case, the classifier is trained upon example texts. We investigated three aspects of text classifiers when categorizing magazine articles with broad subject descriptors: feature selection, learning algorithms, and improvem

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Automatic Indexing: The Assignment of Controlled Language Index Termsr a limited number of good instances are available, the desired patterns will be identified based upon re-occurring features, and noise will be neglected. However, in text classification the number of features is enormous and many features have no relevance. In addition, the number of positive examp

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The Assignment of Subject Descriptors to Magazine Articlesthe term selection process is useful for certain text classes. Given the limited number of positive examples and the high number of text features in the articles that belong to a variety of magazines, columns, and subject domains, the results of training a text classifier with the χ. algorithm are v