Unfolding the Structure of a Document using Deep Learning
| dc.contributor.author | Rahman, Muhammad Mahbubur | |
| dc.contributor.author | Finin, Tim | |
| dc.date.accessioned | 2019-11-22T18:08:49Z | |
| dc.date.available | 2019-11-22T18:08:49Z | |
| dc.date.issued | 2019-09-29 | |
| dc.description.abstract | Understanding and extracting of information from large documents, such as business opportunities, academic articles, medical documents and technical reports, poses challenges not present in short documents. Such large documents may be multi-themed, complex, noisy and cover diverse topics. We describe a framework that can analyze large documents and help people and computer systems locate desired information in them. We aim to automatically identify and classify different sections of documents and understand their purpose within the document. A key contribution of our research is modeling and extracting the logical and semantic structure of electronic documents using deep learning techniques. We evaluate the effectiveness and robustness of our framework through extensive experiments on two collections: more than one million scholarly articles from arXiv and a collection of requests for proposal documents from government sources. | en_US |
| dc.description.sponsorship | This work was partially supported by National Science Foundation grant 1549697 and gifts from IBM and Northrop Grumman. | en_US |
| dc.description.uri | https://arxiv.org/abs/1910.03678 | en_US |
| dc.format.extent | 16 pages | en_US |
| dc.genre | journal articles preprints | en_US |
| dc.identifier | doi:10.13016/m2b3fr-iigx | |
| dc.identifier.citation | Rahman, Muhammad Mahbubur; Finin, Tim; Unfolding the Structure of a Document using Deep Learning; Computation and Language; https://arxiv.org/abs/1910.03678; | en_US |
| dc.identifier.uri | http://hdl.handle.net/11603/16515 | |
| dc.language.iso | en_US | en_US |
| dc.relation.isAvailableAt | The University of Maryland, Baltimore County (UMBC) | |
| dc.relation.ispartof | UMBC Computer Science and Electrical Engineering Department Collection | |
| dc.relation.ispartof | UMBC Faculty Collection | |
| dc.rights | This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author. | |
| dc.subject | document structure | en_US |
| dc.subject | deep learning | en_US |
| dc.subject | document understanding | en_US |
| dc.subject | semantic annotation | en_US |
| dc.title | Unfolding the Structure of a Document using Deep Learning | en_US |
| dc.type | Text | en_US |