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Text mining and natural language processing techniques can be used to extract useful information from text data. Common text mining tasks include text categorization to classify documents into predefined categories, document clustering to group similar documents without predefined categories, and keyword-based association analysis to find frequently co-occurring terms. Text classification algorithms such as support vector machines, naive Bayes classifiers, and neural networks are often applied to categorized documents. The vector space model is commonly used to represent documents as vectors of term weights.

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Text mining and natural language processing techniques can be used to extract useful information from text data. Common text mining tasks include text categorization to classify documents into predefined categories, document clustering to group similar documents without predefined categories, and keyword-based association analysis to find frequent patterns and relationships between keywords in a collection of documents. Text classification algorithms such as support vector machines, k-nearest neighbors, naive Bayes, and neural networks can be applied to categorize documents based on their contents.

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Text mining and natural language processing techniques can be used to extract useful information from text data. Common text mining tasks include text categorization to classify documents into predefined categories, document clustering to group similar documents without predefined categories, and keyword-based association analysis to find frequently co-occurring terms. Text classification algorithms such as support vector machines, naive Bayes classifiers, and neural networks are often applied to categorized documents into topics. The vector space model is commonly used to represent documents as vectors of term weights to enable similarity comparisons between documents.

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The document describes contributions to building a corpora-flow system, including tools for book cleaning, detecting duplicates and candidate pairs, book synchronization, alignment evaluation, and a corpora-flow system. It outlines challenges in corpora building like format issues and provides examples of book processing problems with solutions. Key steps in the tools involve generating a sections ontology, measuring similarity to find duplicates and pairs, matching section delimiters for synchronization, and comparing alignments.

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Text::Perfide::BookCleaner, a Perl module to clean and normalize plain text b...

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This document presents Text::Perfide::BookCleaner, a Perl module for preprocessing plain text books to clean them and prepare them for tasks like text alignment. The module handles various cleaning tasks through a multi-step pipeline, including removing page numbers, headers, footnotes, and normalizing formatting. It uses declarative objects like ontologies and configuration files to guide the cleaning process. An evaluation was performed comparing alignments with and without the module, and results are presented.

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