Understanding and representing the semantics of large structured documents
Links to Fileshttps://ebiquity.umbc.edu/paper/html/id/830/Understanding-and-representing-the-semantics-of-large-structured-documents
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Type of Work12 pages
conference paper pre-print
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natural language processing
UMBC Ebiquity Research Group
Understanding large, structured documents like scholarly articles, requests for proposals or business reports is a complex and difficult task. It involves discovering a document's overall purpose and subject(s), understanding the function and meaning of its sections and subsections, and extracting low level entities and facts about them. In this research, we present a deep learning based document ontology to capture the general purpose semantic structure and domain specific semantic concepts from a large number of academic articles and business documents. The ontology is able to describe different functional parts of a document, which can be used to enhance semantic indexing for a better understanding by human beings and machines. We evaluate our models through extensive experiments on datasets of scholarly articles from arXiv and Request for Proposal documents.