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Research Paper | Information Technology | Volume 6 Issue 3, March 2017 | Pages: 2432 - 2434 | India
Temporal Document Classification Based on Year-Level Timeline Extraction
Abstract: The rapid growth of digital textual data such as news articles, historical archives, blogs, and reports has created a need for efficient organization and retrieval mechanisms. One important aspect of document organization is temporal classification, which involves identifying the time period associated with a document. Temporal information embedded in documents may appear explicitly as dates or implicitly through contextual references to events. This paper presents a machine learning-based framework for classifying documents into specific year-based timelines based on their temporal content. The proposed system extracts temporal expressions, performs text preprocessing, and applies feature extraction techniques to represent documents numerically. A supervised classification model is then trained to assign documents to the most relevant year category. Experimental evaluation demonstrates that the proposed approach effectively organizes documents into chronological timelines and improves information retrieval in large document repositories.
Keywords: Temporal Text Mining, Document Classification, Timeline Extraction, Machine Learning, Information Retrieval
How to Cite?: Patel Parul, "Temporal Document Classification Based on Year-Level Timeline Extraction", Volume 6 Issue 3, March 2017, International Journal of Science and Research (IJSR), Pages: 2432-2434, https://www.ijsr.net/getabstract.php?paperid=SR17310143947, DOI: https://dx.dx.doi.org/10.21275/SR17310143947