Deep Understanding of a Document's Structure
| dc.contributor.author | Rahman, Muhammad Mahbubur | |
| dc.contributor.author | Finin, Tim | |
| dc.date.accessioned | 2018-10-17T16:45:31Z | |
| dc.date.available | 2018-10-17T16:45:31Z | |
| dc.date.issued | 2017-12-05 | |
| dc.description | 4th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies | en_US |
| dc.description.abstract | Current language understanding approaches focus on small documents, such as newswire articles, blog posts, product reviews and discussion forum discussions. Understanding and extracting information from large documents like legal briefs, proposals, technical manuals and research articles is still a challenging task. We describe a framework that can analyze a large document and help people to locate desired information in it. 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 structure of electronic documents using machine learning techniques, including deep learning. We also make available a dataset of information about a collection of scholarly articles from the arXiv eprints collection that includes a wide range of metadata for each article, including a table of contents, section labels, section summarizations and more. We hope that this dataset will be a useful resource for the machine learning and language understanding communities for information retrieval, content-based question answering and language modeling tasks. | en_US |
| dc.description.sponsorship | e work presented in this paper was partially supported by a grant number 1549697 from the National Science Foundation(NSF). | |
| dc.description.uri | https://dl.acm.org/citation.cfm?id=3148055.3148080 | en_US |
| dc.format.extent | 11 pages | en_US |
| dc.genre | conference paper pre-print | en_US |
| dc.identifier | doi:10.13016/M2PK07558 | |
| dc.identifier.uri | https://doi.org/10.1145/3148055.3148080 | |
| dc.identifier.uri | http://hdl.handle.net/11603/11581 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | ACM | |
| 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.relation.ispartof | UMBC Student 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 | deep learning | en_US |
| dc.subject | learning | en_US |
| dc.subject | natural language processing | en_US |
| dc.subject | UMBC Ebiquity Research Group | en_US |
| dc.title | Deep Understanding of a Document's Structure | en_US |
| dc.type | Text | en_US |