2007 International Conference on Machine Learning and Cybernetics 2007
DOI: 10.1109/icmlc.2007.4370650
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SVM-Based Classification Method for Poetry Style

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Cited by 13 publications
(7 citation statements)
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“…Literary Pieces, lyrics and unsolicited bulk mails [27] can be mined for views/ feelings/emotions. Liu [1,3,9,11,16,17,22,23,25], Naïve Bayes (NB) [1,3,4,17,19,23], K-Nearest Neighbor (KNN) [14,23,26], Maximum Entropy (ME) [3], Winnow Classifier [21] and Centroid [21] were experimented by different research on different kinds of dataset.…”
Section: Literature Surveymentioning
confidence: 99%
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“…Literary Pieces, lyrics and unsolicited bulk mails [27] can be mined for views/ feelings/emotions. Liu [1,3,9,11,16,17,22,23,25], Naïve Bayes (NB) [1,3,4,17,19,23], K-Nearest Neighbor (KNN) [14,23,26], Maximum Entropy (ME) [3], Winnow Classifier [21] and Centroid [21] were experimented by different research on different kinds of dataset.…”
Section: Literature Surveymentioning
confidence: 99%
“…Support Vector Machine (SVM) based method is used to differentiate bold-and-unconstrained style from graceful-andrestrained style of poetry as presented in He Z.S. [25]. In this work, a piece of poetry is expressed using Vector Space Model (VSM) first, and then information gain is used to select the poetry's feature terms.…”
Section: Formal Text Corpusmentioning
confidence: 99%
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“…1 Background NLP for poetry NLP for poetry has mainly focused on stylistic analysis (Hayward 1996;Kaplan and Blei 2007;He et al 2007;Fang, Lo, and Chinn 2009;Greene, Bodrumlu, and Knight 2010;Genzel, Uszkoreit, and Och 2010;Kao and Jurafsky 2012) and poetry generation (Manurung, Ritchie, and Thompson 2012;Zhang and Lapata 2014;Ghazvininejad et al 2016). Research on stylistics has focused on the features that make a poem come across as poetic (Kao and Jurafsky 2012); on quantifying poetic devices such as rhyme and meter (Hayward 1996;Greene, Bodrumlu, and Knight 2010;Genzel, Uszkoreit, and Och 2010); on evidence of intertextuality and how to prove stylistic influence between authors (Forstall, Jacobson, and Scheirer 2011); or on authorship and style attribution (Kaplan and Blei 2007;He et al 2007;Fang, Lo, and Chinn 2009). These studies are examples of detecting statistical regularities in poetic language, potentially helping us to better understand and categorize poetic literature (Fabb 2006).…”
Section: Introductionmentioning
confidence: 99%
“…In this paper, we propose a psycholinguistic framework for analyzing textual features that may be responsible for the poetic beauty in the poems of King Henry VIII. Previous research focused on quantifying poetic devices such as rhyme and meter (Hayward, 1996;Greene et al, 2010;Genzel et al, 2010), tracking stylistic influence between authors (Forstall et al, 2011), or classifying poems based on the poet and style (Kaplan & Blei, 2007;He et al, 2007;Fang et al, 2009). In this paper, we aim to show the affect of the psychological condition of King Henry VIII on his writings (Poems and lyrics).…”
Section: Introductionmentioning
confidence: 99%