Notice

This is not the latest version of this item. The latest version can be found at:https://dspace.mit.edu/handle/1721.1/137699.2

Show simple item record

dc.contributor.authorJin, D
dc.contributor.authorSzolovits, P
dc.date.accessioned2021-11-08T16:16:59Z
dc.date.available2021-11-08T16:16:59Z
dc.date.issued2018-10
dc.identifier.urihttps://hdl.handle.net/1721.1/137699
dc.description.abstract© 2018 Association for Computational Linguistics Prevalent models based on artificial neural network (ANN) for sentence classification often classify sentences in isolation without considering the context in which sentences appear. This hampers the traditional sentence classification approaches to the problem of sequential sentence classification, where structured prediction is needed for better overall classification performance. In this work, we present a hierarchical sequential labeling network to make use of the contextual information within surrounding sentences to help classify the current sentence. Our model outperforms the state-of-the-art results by 2%-3% on two benchmarking datasets for sequential sentence classification in medical scientific abstracts.en_US
dc.language.isoen
dc.relation.isversionofhttps://www.aclweb.org/anthology/D18-1349/en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alikeen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en_US
dc.sourcearXiven_US
dc.titleHierarchical neural networks for sequential sentence classification in medical scientific abstractsen_US
dc.typeArticleen_US
dc.identifier.citationJin, D and Szolovits, P. 2018. "Hierarchical neural networks for sequential sentence classification in medical scientific abstracts." Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018.
dc.relation.journalProceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018en_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
eprint.statushttp://purl.org/eprint/status/NonPeerRevieweden_US
dc.date.updated2021-01-26T19:52:23Z
dspace.orderedauthorsJin, D; Szolovits, Pen_US
dspace.date.submission2021-01-26T19:52:25Z
mit.licenseOPEN_ACCESS_POLICY
mit.metadata.statusAuthority Work and Publication Information Neededen_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record

VersionItemDateSummary

*Selected version