Research on Named Entity Recognition Based on Gated Interaction Mechanisms Article Swipe
Bin Liu
,
Wanyuan Chen
,
Jialing Tao
,
Lei He
,
Dan Tang
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/app14156481
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/app14156481
Using long short-term memory (LSTM) networks to build a named entity recognition model is important for the task of named entity recognition. However, traditional memory networks lack a direct connection between input information and hidden states, leading to key feature information not being fully learned during training and causing information loss. This paper designs a bidirectional variant of the long short-term memory (BiLSTM) network called Mogrifier-BiGRU, which combines the BERT pre-trained model and the conditional random field (CRF) network model. The Mogrifier gating interaction unit is set with more hyperparameters to achieve deep interaction of gating information, changing the relationship between input and hidden states so that they are no longer independent. By introducing more nonlinear transformations, the model can learn more complex input–output mapping relationships. Then, by combining Bayesian optimization with the improved Mogrifier-BiGRU network, the optimal hyperparameters of the model are automatically calculated. Experimental results show that the model method based on the gating interaction mechanism can effectively combine feature information, improving the accuracy of Chinese-named entity recognition. On the dataset, an F1-score of 85.42% was achieved, which is 7% higher than traditional methods and 10% higher for the accuracy of some entity recognition.
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- Language
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- Landing Page
- https://doi.org/10.3390/app14156481
- OA Status
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- References
- 19
- Related Works
- 10
- OpenAlex ID
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- DOI
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https://doi.org/10.3390/app14156481Digital Object Identifier
- Title
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Research on Named Entity Recognition Based on Gated Interaction MechanismsWork title
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articleOpenAlex work type
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-07-25Full publication date if available
- Authors
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Bin Liu, Wanyuan Chen, Jialing Tao, Lei He, Dan TangList of authors in order
- Landing page
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https://doi.org/10.3390/app14156481Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
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Computer science, Artificial intelligenceTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.key | 38 |
| abstract_inverted_index.not | 41 |
| abstract_inverted_index.set | 86 |
| abstract_inverted_index.the | 16, 58, 68, 73, 98, 117, 132, 136, 140, 149, 154, 164, 171, 190 |
| abstract_inverted_index.was | 177 |
| abstract_inverted_index.BERT | 69 |
| abstract_inverted_index.This | 51 |
| abstract_inverted_index.deep | 92 |
| abstract_inverted_index.lack | 26 |
| abstract_inverted_index.long | 1, 59 |
| abstract_inverted_index.more | 88, 114, 121 |
| abstract_inverted_index.show | 147 |
| abstract_inverted_index.some | 193 |
| abstract_inverted_index.task | 17 |
| abstract_inverted_index.than | 183 |
| abstract_inverted_index.that | 106, 148 |
| abstract_inverted_index.they | 107 |
| abstract_inverted_index.unit | 84 |
| abstract_inverted_index.with | 87, 131 |
| abstract_inverted_index.(CRF) | 77 |
| abstract_inverted_index.Then, | 126 |
| abstract_inverted_index.Using | 0 |
| abstract_inverted_index.based | 152 |
| abstract_inverted_index.being | 42 |
| abstract_inverted_index.build | 7 |
| abstract_inverted_index.field | 76 |
| abstract_inverted_index.fully | 43 |
| abstract_inverted_index.input | 31, 101 |
| abstract_inverted_index.learn | 120 |
| abstract_inverted_index.loss. | 50 |
| abstract_inverted_index.model | 12, 71, 118, 141, 150 |
| abstract_inverted_index.named | 9, 19 |
| abstract_inverted_index.paper | 52 |
| abstract_inverted_index.which | 66, 179 |
| abstract_inverted_index.(LSTM) | 4 |
| abstract_inverted_index.85.42% | 176 |
| abstract_inverted_index.called | 64 |
| abstract_inverted_index.direct | 28 |
| abstract_inverted_index.during | 45 |
| abstract_inverted_index.entity | 10, 20, 168, 194 |
| abstract_inverted_index.gating | 82, 95, 155 |
| abstract_inverted_index.hidden | 34, 103 |
| abstract_inverted_index.higher | 182, 188 |
| abstract_inverted_index.longer | 110 |
| abstract_inverted_index.memory | 3, 24, 61 |
| abstract_inverted_index.method | 151 |
| abstract_inverted_index.model. | 79 |
| abstract_inverted_index.random | 75 |
| abstract_inverted_index.states | 104 |
| abstract_inverted_index.achieve | 91 |
| abstract_inverted_index.between | 30, 100 |
| abstract_inverted_index.causing | 48 |
| abstract_inverted_index.combine | 160 |
| abstract_inverted_index.complex | 122 |
| abstract_inverted_index.designs | 53 |
| abstract_inverted_index.feature | 39, 161 |
| abstract_inverted_index.leading | 36 |
| abstract_inverted_index.learned | 44 |
| abstract_inverted_index.mapping | 124 |
| abstract_inverted_index.methods | 185 |
| abstract_inverted_index.network | 63, 78 |
| abstract_inverted_index.optimal | 137 |
| abstract_inverted_index.results | 146 |
| abstract_inverted_index.states, | 35 |
| abstract_inverted_index.variant | 56 |
| abstract_inverted_index.(BiLSTM) | 62 |
| abstract_inverted_index.Bayesian | 129 |
| abstract_inverted_index.F1-score | 174 |
| abstract_inverted_index.However, | 22 |
| abstract_inverted_index.accuracy | 165, 191 |
| abstract_inverted_index.changing | 97 |
| abstract_inverted_index.combines | 67 |
| abstract_inverted_index.dataset, | 172 |
| abstract_inverted_index.improved | 133 |
| abstract_inverted_index.network, | 135 |
| abstract_inverted_index.networks | 5, 25 |
| abstract_inverted_index.training | 46 |
| abstract_inverted_index.Mogrifier | 81 |
| abstract_inverted_index.achieved, | 178 |
| abstract_inverted_index.combining | 128 |
| abstract_inverted_index.important | 14 |
| abstract_inverted_index.improving | 163 |
| abstract_inverted_index.mechanism | 157 |
| abstract_inverted_index.nonlinear | 115 |
| abstract_inverted_index.connection | 29 |
| abstract_inverted_index.short-term | 2, 60 |
| abstract_inverted_index.calculated. | 144 |
| abstract_inverted_index.conditional | 74 |
| abstract_inverted_index.effectively | 159 |
| abstract_inverted_index.information | 32, 40, 49 |
| abstract_inverted_index.interaction | 83, 93, 156 |
| abstract_inverted_index.introducing | 113 |
| abstract_inverted_index.pre-trained | 70 |
| abstract_inverted_index.recognition | 11 |
| abstract_inverted_index.traditional | 23, 184 |
| abstract_inverted_index.Experimental | 145 |
| abstract_inverted_index.independent. | 111 |
| abstract_inverted_index.information, | 96, 162 |
| abstract_inverted_index.optimization | 130 |
| abstract_inverted_index.recognition. | 21, 169, 195 |
| abstract_inverted_index.relationship | 99 |
| abstract_inverted_index.Chinese-named | 167 |
| abstract_inverted_index.automatically | 143 |
| abstract_inverted_index.bidirectional | 55 |
| abstract_inverted_index.input–output | 123 |
| abstract_inverted_index.relationships. | 125 |
| abstract_inverted_index.Mogrifier-BiGRU | 134 |
| abstract_inverted_index.hyperparameters | 89, 138 |
| abstract_inverted_index.Mogrifier-BiGRU, | 65 |
| abstract_inverted_index.transformations, | 116 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5007019142 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 5 |
| corresponding_institution_ids | https://openalex.org/I24201400, https://openalex.org/I4210139481 |
| citation_normalized_percentile.value | 0.11606588 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |