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3 days ago by cdrago
Understanding and representing the semantics of large structured documents
Muhammad Mahbubur Rahman and Tim Finin
University of Maryland, Baltimore County, Baltimore MD 21250, USA {mrahman1,finin}@umbc.edu
Abstract. 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.
nlp  deeplearning 
3 days ago by hustwj

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