Proceedings of the 4th Workshop on Semantic Deep Learning (SemDeep-4, ISWC)

Understanding and representing the semantics of large structured documents

Muhammad Mahbubur Rahman and Tim Finin

October 8, 2018

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 can describe different functional parts of a document and can be used to enhance semantic indexing for better understanding by humans and machines. We evaluate our models through extensive experiments on datasets of scholarly articles from arXiv and Request for Proposal documents.

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Tags:
deep learning,
semantic annotation, ai, document ontology, learning, natural language processing, ontology

Type: InProceedings

Publisher: CEUR Workshop Proceedings

Organization: CEUR

Volume: 2241

Note:

Downloads: 1324 downloads

Google Scholar Citations: 27 citations