Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/162082
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dc.contributor.authorLi, Qien_US
dc.contributor.authorMao, Kezhien_US
dc.contributor.authorLi, Pengfeien_US
dc.contributor.authorXu, Yuecongen_US
dc.contributor.authorLo, Edmond Yat Manen_US
dc.date.accessioned2022-10-04T01:57:50Z-
dc.date.available2022-10-04T01:57:50Z-
dc.date.issued2022-
dc.identifier.citationLi, Q., Mao, K., Li, P., Xu, Y. & Lo, E. Y. M. (2022). A novel end-to-end neural network for simultaneous filtering of task-unrelated named entities and fine-grained typing of task-related named entities. Expert Systems With Applications, 204, 117498-. https://dx.doi.org/10.1016/j.eswa.2022.117498en_US
dc.identifier.issn0957-4174en_US
dc.identifier.urihttps://hdl.handle.net/10356/162082-
dc.description.abstractRecently, one emerging problem in Named Entity Typing (NET) is the fine-grained classification of task-related entities co-existing with task-unrelated entities. The traditional pipeline framework decomposes this problem into two sub-tasks. The first sub-task filters out the task-unrelated entities, while the second sub-task performs fine-grained classification for task-related entities. In the present study, we have developed an end-to-end neural network to solve the two sub-tasks simultaneously. The new model has two main merits. First, Mention–Mention (MM) relationship learning is developed to capture the interaction of task related and unrelated entities for producing more discriminative features. Second, an Improved Radial Basis Function classifier (ImRBF) with a novel training scheme is developed to jointly solve task-unrelated entity filtering and fine-grained classification of task-related entities. Experiments show that our model outperforms the pipeline methods by 3.3%–6% (F1 score) on the first sub-task and 1.8%–6.3% (F1 score) on the second sub-task.en_US
dc.language.isoenen_US
dc.relation.ispartofExpert Systems with Applicationsen_US
dc.rights© 2022 Published by Elsevier Ltd. All rights reserved.en_US
dc.subjectEngineering::Electrical and electronic engineeringen_US
dc.titleA novel end-to-end neural network for simultaneous filtering of task-unrelated named entities and fine-grained typing of task-related named entitiesen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.contributor.schoolInterdisciplinary Graduate School (IGS)en_US
dc.contributor.researchInstitute of Catastrophe Risk Managementen_US
dc.identifier.doi10.1016/j.eswa.2022.117498-
dc.identifier.scopus2-s2.0-85130627192-
dc.identifier.volume204en_US
dc.identifier.spage117498en_US
dc.subject.keywordsNamed Entity Typingen_US
dc.subject.keywordsFine-Graineden_US
item.grantfulltextnone-
item.fulltextNo Fulltext-
Appears in Collections:EEE Journal Articles
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