A learning approach towards metre-based classification of similar Hindi poems using proposed two-level data transformation.
With the advancement in technology and digitalization of resources, computation of humanities problems is no exception to remain untouched. Automatic poetry classification is now a well-defined problem which can be solved using various approaches. Mood-based poetry classification is one of the popul...
| Publicado en: | Digital Scholarship in the Humanities Vol. 38; no. 3; pp. 1166 - 1183 |
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| Autores principales: | , |
| Formato: | Artículo |
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Oxford University Press / USA
Sep2023
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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=171389421&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 171389421 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 2055768X JEO9 jtl: Digital Scholarship in the Humanities issn: 2055768X maglogo: N pubinfo: dt: Sep2023 vid: 38 iid: 3 pid: 622 pub: Oxford University Press / USA artinfo: ui: 171389421 10.1093/llc/fqad011 ppf: 1166 ppct: 17 formats: fmt: – @attributes: type: T – @attributes: type: P size: 1.8MB tig: atl: A learning approach towards metre-based classification of similar Hindi poems using proposed two-level data transformation. aug: au: Naaz, Komal Singh, Niraj Kumar affil: Department of Computer Science and Engineering, Birla Institute of Technology , Mesra 835215, Jharkhand, India su: Machine learning Hebbian memory Automatic classification Feature extraction Support vector machines Random forest algorithms sug: subj: Machine learning Hebbian memory Automatic classification Feature extraction Support vector machines Random forest algorithms ab: With the advancement in technology and digitalization of resources, computation of humanities problems is no exception to remain untouched. Automatic poetry classification is now a well-defined problem which can be solved using various approaches. Mood-based poetry classification is one of the popular ones. We propose a learning approach towards metre-based classification of Hindi metrical poetry. The state of art model for the metre-based poetry classification uses the rule-based approach whereas the proposed system uses learning models to perform classification. Feature extraction and classification are the two main components of text classification in natural language processing. Text is transformed into machine-readable numbers through the process of feature extraction, which is subsequently submitted to classification models. Poems, in their most natural formulation, are unfit to any learning-based algorithms. However, transforming the data into certain form and selecting a fixed number of features out of it (feature extraction) made the classification possible using machine learning approach which was yet untouched and can act as benchmark for the concerned area of research. The article deals with six popular and similar types of Hindi poems. The dataset is collected and processed to form an early dataset that undergoes two levels of data transformation and feature engineering, resulting in the pre-processed dataset. The pre-processed dataset is then fed as input to selected machine learning models (Bernoulli Naïve Bayes, k -nearest neighbour, random forest, and support vector machine) producing classification result with best accuracy of 99%, that further undergoes a post-processing step based on observed misclassifications. 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: 2023 holdings: @attributes: islocal: N |
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