Recurrent convolutional neural networks for poet identification.
Deep neural networks have been widely used in various language processing tasks. Recurrent neural networks (RNNs) and convolutional neural networks (CNN) are two common types of neural networks that have a successful history in capturing temporal and spatial features of texts. By using RNN, we can e...
| Publicado en: | Digital Scholarship in the Humanities Vol. 36; no. 2; pp. 472 - 482 |
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| Autores principales: | , |
| Formato: | Artículo |
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Oxford University Press / USA
Jun2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=152743603&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 152743603 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 2055768X JEO9 jtl: Digital Scholarship in the Humanities issn: 2055768X maglogo: N pubinfo: dt: Jun2021 vid: 36 iid: 2 pid: 622 pub: Oxford University Press / USA artinfo: ui: 152743603 10.1093/llc/fqz096 ppf: 472 ppct: 10 formats: fmt: @attributes: type: P size: 493KB tig: atl: Recurrent convolutional neural networks for poet identification. aug: au: Salami, Dariush Momtazi, Saeedeh affil: Department of Communication and Networking, School of Electrical Engineering, Aalto University , Finland Computer Engineering Department, Amirkabir University of Technology , Tehran, Iran su: Convolutional neural networks Recurrent neural networks Artificial neural networks Support vector machines Deep learning Poets sug: subj: Convolutional neural networks Recurrent neural networks Artificial neural networks Support vector machines Deep learning Poets ab: Deep neural networks have been widely used in various language processing tasks. Recurrent neural networks (RNNs) and convolutional neural networks (CNN) are two common types of neural networks that have a successful history in capturing temporal and spatial features of texts. By using RNN, we can encode input text to a lower space of semantic features while considering the sequential behavior of words. By using CNN, we can transfer the representation of input text to a flat structure to be used for classifying text. In this article, we proposed a novel recurrent CNN model to capture not only the temporal but also the spatial features of the input poem/verse to be used for poet identification. Considering the shortcomings of the normal RNNs, we try both long short-term memory and gated recurrent unit units in the proposed architecture and apply them to the poet identification task. There are a large number of poems in the history of literature whose poets are unknown. Considering the importance of the task in the information processing field, a great variety of methods from traditional learning models, such as support vector machine and logistic regression, to deep neural network models, such as CNN, have been proposed to address this problem. Our experiments show that the proposed model significantly outperforms the state-of-the-art models for poet identification by receiving either a poem or a single verse as input. In comparison to the state-of-the-art CNN model, we achieved 9% and 4% improvements in f-measure for poem- and verse-based tasks, respectively. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: © 2019 EADH: The European Association for Digital Humanities. item: Digital Scholarship in the Humanities holder: Oxford University Press / USA dt: @attributes: year: 2021 holdings: @attributes: islocal: N |
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