Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/162082
Title: A novel end-to-end neural network for simultaneous filtering of task-unrelated named entities and fine-grained typing of task-related named entities
Authors: Li, Qi
Mao, Kezhi
Li, Pengfei
Xu, Yuecong
Lo, Edmond Yat Man
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2022
Source: Li, 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.2022.117498
Journal: Expert Systems with Applications
Abstract: Recently, 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.
URI: https://hdl.handle.net/10356/162082
ISSN: 0957-4174
DOI: 10.1016/j.eswa.2022.117498
Rights: © 2022 Published by Elsevier Ltd. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:EEE Journal Articles
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