The Poet Identification Using Convolutional Neural Networks

Waijanya, Sajjaporn; Promrit, Nuttachot

doi:10.1007/978-3-319-60663-7_17

Cited by 6 publications

(6 citation statements)

References 3 publications

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“…Finally, in one of the most recent works of the poet identification task, Waijanya and Promrit (2018) proposed a model using CNN to identify poets of Thai poems. They extracted features automatically using CNN and fed them to a fully connected layer followed by a softmax layer.…”

Section: Related Workmentioning

confidence: 99%

“…Finally, as the last baseline model, we use one of the most recent proposed models in poet identification. The model is a CNN based model which has been used for identifying the poet of Thai poems by Waijanya and Promrit (2018).…”

Section: Baseline Modelsmentioning

confidence: 99%

“…Poet identification is one of the sub-fields of the authorship identification in which we can use some other features including rhyme and prosody (Shahmiri and Ghidary, 2006). These features can be manually extracted (Shahmiri and Ghidary, 2006) or can be extracted using a deep neural network (Waijanya and Promrit, 2018).…”

Section: Introductionmentioning

confidence: 99%

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Recurrent convolutional neural networks for poet identification

Salami

Momtazi

2020

Digital Scholarship in the Humanities

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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.

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Section: Related Workmentioning

confidence: 99%

Section: Baseline Modelsmentioning

confidence: 99%

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Recurrent convolutional neural networks for poet identification

Salami

Momtazi

2020

Digital Scholarship in the Humanities

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“…The structure of Klon-Suphap External Rhyme Prosody is shown in Fig. 1 [1]. External Rhyme occurs in relation between Wak and another, forward to each other, and Rhyme between chapters in other poems.…”

Section: Introductionmentioning

confidence: 99%

The extraction of beautiful sound patterns from Sunthorn Phu’s poem using machine learning technique and internal rhyme rule

Suksanguan

Waijanya

Promrit

2021

Int. J. Adv. Intell. Informatics

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The melodious poems have been written from the distinctive features of poetry or based on each country's typical style. Especially, Thai poems which composed by the use of specific forming, such as Internal Rhyme to develop melodiousness. The most attractive and well-known poems were composed by a genius Thai poet named Sunthorn Phu. He is a role model for Thai poets. UNESCO honored him as the world’s great poet and the best role model in poetry works. In this article, we proposed extracting 15,796 sentences (Waks) of the beautiful sound patterns of Phra Aphai Mani’s tales by machine learning technology in conjunction with the rules of internal Rhyme Klon-Suphap by using the Apriori Algorithm. The extraction of vowel rhymes separated by a group of Waks including 1) Poem Wak No. 1; 2) Poem Wak No. 2; 3) Poem Wak No. 3; and 4) Poem Wak No. 4. In this article, “Wak” means sentence. The created tool can extract the internal rhyme patterns and the 25 popular pattern vowels. The popular pattern illustrates the melodiousness of the Poem and sets up a standard of how to melodiously compose a poem. Then, the evaluation of the experiments was done by using 144 Waks selected from the extraction of the beautiful patterns and evaluated by the consistency score from 3 experts. The average accuracy score resulted in 95.30%.

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“…Thus using the attributes of the data is critical for the transfer learning (Kulkarni, Sharma, Zepeda and Chevallier, 2014;Peng, Tian, Xiang, Wang, Pontil and Huang, 2017;Suzuki, Sato, Oyama and Kurihara, 2014a,b). In this paper, we study the problem of effective use of both input data and attribute for the domain-transfer learning problem, and propose a novel method of attribute embedding based on the popular convolutional neural network (CNN) (Fujino, Hatanaka, Mori and Matsumoto, 2018;Geng, Liang, Li, Wang, Liang, Xu and Wang, 2016;Geng, Zhang, Li, Gu, Liang, Liang, Wang, Wu, Patil and Wang, 2017;Jing, Zhao, Li and Xu, 2017;Puri, Tewari, Katyal and Garg, 2018;Roa-Barco, Serradilla-Casado, Velasco-Vzquez, Lpez-Zorrilla, Gra?a, Chyzhyk and Price, 2018;Shen, Zhou, Yang, Yang and Tian, 2015;Shen, Zhou, Yang, Yu, Dong, Yang, Zang and Tian, 2017;Todoroki, Han, Iwamoto, Lin, Hu and Chen, 2018;Waijanya and Promrit, 2018;Zhang, Liang, Li, Fang, Wang, Geng and Wang, 2017a) to solve this problem. Further, we develop a novel model using the attribute embedding as the input for the learning of the target domain classification model.…”

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confidence: 99%

Domain transfer convolutional attribute embedding

Wang

2018

Journal of Experimental & Theoretical Artificial Intelligen

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In this paper, we study the problem of transfer learning with the attribute data. In the transfer learning problem, we want to leverage the data of the auxiliary and the target domains to build an effective model for the classification problem in the target domain. Meanwhile, the attributes are naturally stable cross different domains. This strongly motives us to learn effective domain transfer attribute representations. To this end, we proposed to embed the attributes of the data to a common space by using the powerful convolutional neural network (CNN) model. The convolutional representations of the data points are mapped to the corresponding attributes so that they can be effective embedding of the attributes. We also represent the data of different domains by a domain-independent CNN, ant a domain-specific CNN, and combine their outputs with the attribute embedding to build the classification model. An joint learning framework is constructed to minimize the classification errors, the attribute mapping error, the mismatching of the domain-independent representations cross different domains, and to encourage the the neighborhood smoothness of representations in the target domain. The minimization problem is solved by an iterative algorithm based on gradient descent. Experiments over benchmark data sets of person re-identification, bankruptcy prediction, and spam email detection, show the effectiveness of the proposed method.

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The Poet Identification Using Convolutional Neural Networks

Cited by 6 publications

References 3 publications

Recurrent convolutional neural networks for poet identification

Recurrent convolutional neural networks for poet identification

The extraction of beautiful sound patterns from Sunthorn Phu’s poem using machine learning technique and internal rhyme rule

Domain transfer convolutional attribute embedding

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