Does semantics aid syntax? An empirical study on named entity recognition and classification
Many researchers jointly model multiple linguistic tasks (e.g., joint modeling of named entity recognition and named entity classification and joint modeling of syntactic parsing and semantic parsing) with an implicit assumption that these individual tasks can enhance each other via the joint modeli...
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sg-ntu-dr.10356-1607022022-08-01T05:20:29Z Does semantics aid syntax? An empirical study on named entity recognition and classification Zhong, Xiaoshi Cambria, Erik Hussain, Amir School of Computer Science and Engineering Engineering::Computer science and engineering Semantics Syntax Many researchers jointly model multiple linguistic tasks (e.g., joint modeling of named entity recognition and named entity classification and joint modeling of syntactic parsing and semantic parsing) with an implicit assumption that these individual tasks can enhance each other via the joint modeling. Before conducting research on jointly modeling multiple tasks, however, such researchers hardly examine whether such assumption is true or not. In this paper, we empirically examine whether named entity classification improves the performance of named entity recognition as an empirical case of examining whether semantics improves the performance of a syntactic task. To this end, we firstly specify the way to determine whether a linguistic task is a syntactic task or a semantic task according to both syntactic theory and semantic theory. After that, we design and conduct extensive experiments on two well-known benchmark datasets using three representative yet diverse state-of-the-art models. Experimental results demonstrate that named entity recognition does not lie at the semantic level and is not a semantic task; instead, it is a syntactic task and that the joint modeling of named entity recognition and classification does not improve the performance of named entity recognition. Experimental results also demonstrate that traditional handcrafted feature models can achieve state-of-the-art performance in comparison with the auto-learned feature model on named entity recognition. Agency for Science, Technology and Research (A*STAR) This research/project is supported by A*STAR under its Industry Alignment Fund (LOA Award I1901E0046). 2022-08-01T05:20:29Z 2022-08-01T05:20:29Z 2022 Journal Article Zhong, X., Cambria, E. & Hussain, A. (2022). Does semantics aid syntax? An empirical study on named entity recognition and classification. Neural Computing and Applications, 34(11), 8373-8384. https://dx.doi.org/10.1007/s00521-021-05949-0 0941-0643 https://hdl.handle.net/10356/160702 10.1007/s00521-021-05949-0 2-s2.0-85104230059 11 34 8373 8384 en I1901E0046 Neural Computing and Applications © 2021 The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature. All rights reserved. |
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Engineering::Computer science and engineering Semantics Syntax Zhong, Xiaoshi Cambria, Erik Hussain, Amir Does semantics aid syntax? An empirical study on named entity recognition and classification |
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Many researchers jointly model multiple linguistic tasks (e.g., joint modeling of named entity recognition and named entity classification and joint modeling of syntactic parsing and semantic parsing) with an implicit assumption that these individual tasks can enhance each other via the joint modeling. Before conducting research on jointly modeling multiple tasks, however, such researchers hardly examine whether such assumption is true or not. In this paper, we empirically examine whether named entity classification improves the performance of named entity recognition as an empirical case of examining whether semantics improves the performance of a syntactic task. To this end, we firstly specify the way to determine whether a linguistic task is a syntactic task or a semantic task according to both syntactic theory and semantic theory. After that, we design and conduct extensive experiments on two well-known benchmark datasets using three representative yet diverse state-of-the-art models. Experimental results demonstrate that named entity recognition does not lie at the semantic level and is not a semantic task; instead, it is a syntactic task and that the joint modeling of named entity recognition and classification does not improve the performance of named entity recognition. Experimental results also demonstrate that traditional handcrafted feature models can achieve state-of-the-art performance in comparison with the auto-learned feature model on named entity recognition. |
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School of Computer Science and Engineering |
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School of Computer Science and Engineering Zhong, Xiaoshi Cambria, Erik Hussain, Amir |
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Article |
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Zhong, Xiaoshi Cambria, Erik Hussain, Amir |
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Zhong, Xiaoshi |
title |
Does semantics aid syntax? An empirical study on named entity recognition and classification |
title_short |
Does semantics aid syntax? An empirical study on named entity recognition and classification |
title_full |
Does semantics aid syntax? An empirical study on named entity recognition and classification |
title_fullStr |
Does semantics aid syntax? An empirical study on named entity recognition and classification |
title_full_unstemmed |
Does semantics aid syntax? An empirical study on named entity recognition and classification |
title_sort |
does semantics aid syntax? an empirical study on named entity recognition and classification |
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2022 |
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https://hdl.handle.net/10356/160702 |
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1743119476903116800 |