Model for Classification of Poems in Hindi Language Based on Ras

Pal, Kaushika; Patel, Biraj V.

doi:10.1007/978-981-13-8406-6_62

Cited by 18 publications

(5 citation statements)

References 11 publications

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“…Pal and Patel [17] classified poetry based on nine different types of Rasas like Shringar, Hasya, Rudra etc., and used a mix of part-of-speech and emotion-based features to classify poems into different types. In research by Lone et al [18], a Kashmiri-to-English Machine Translation System was presented, as well as it highlighted various features of the Kashmiri language.…”

Section: Bafna and Sainimentioning

confidence: 99%

A Novel Approach for Identification of Figurative Language Types in Devanagari Scripted Languages

Saini,

Sagar,

Gaikwad

2023

IJACSA

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Poetry can be defined as a form of literary expression that uses language and artistic techniques to evoke emotions, create imagery, and convey complex ideas in a concentrated and imaginative manner. It is a form of written or spoken art that often incorporates rhythm, meter, rhyme, and figurative language to engage the reader or listener on multiple levels. There is no automated system that can identify figures of speech (FsoS) in poetry using Natural Language Processing (NLP) methods. In this research paper, the authors categorized four types of FsoS (Type of alliteration), (Type of alliteration), (Rhyme), and (Repetition) using two custom algorithms, Koshur and Awadhi (KA and AA), developed specifically for three different language corpora of poems (Koshur (K), Awadhi (A), and Hindi (H)). To evaluate the effectiveness of these algorithms, the authors conducted tests on three languages using four distinct approaches: with stopwords without optimization, with stopwords with optimization, without stopwords without optimization, and without stopwords with optimization. Authors have put lots of effort into identifying FsoS in not only one single language but in three Devanagari scripted languages. This research work is the first of its kind. The average accuracy without stopwords was not up to the mark. The authors then optimized both algorithms and again tested them on the same corpora with or without stopwords, resulting in a significant increase in accuracy.

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Section: Bafna and Sainimentioning

confidence: 99%

A Novel Approach for Identification of Figurative Language Types in Devanagari Scripted Languages

Saini,

Sagar,

Gaikwad

2023

IJACSA

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“…Pal and Patel [18] researched the development of a model based on the nine 'Ras' and tried to classify it using Machine Learning modeling. Saini and Kaur [19] also did emotion detection-based research focusing on 'Navrasa' using machine learning algorithm Naïve Bayes (NB) and Support Vector Machine (SVM), SVM performed better with 70.02% overall accuracy.…”

Section: Literature Reviewmentioning

confidence: 99%

Stanza Type Identification using Systematization of Versification System of Hindi Poetry

Audichya¹,

Jatinderkumar²

2021

IJACSA

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Poetry covers a vast part of the literature of any language. Similarly, Hindi poetry is also having a massive portion in Hindi literature. In Hindi poetry construction, it is necessary to take care of various verse writing rules. This paper focuses on the automatic metadata generation from such poems by computational linguistics integrated advance and systematic, prosody rule-based modeling and detection procedures specially designed for Hindi poetry. The paper covers various challenges and the best possible solutions for those challenges, describing the methodology to generate automatic metadata for "Chhand" based on the poems' stanzas. It also provides some advanced information and techniques for metadata generation for "Muktak Chhands". Rules of the "Chhands" incorporated in this research were identified, verified, and modeled as per the computational linguistics perspective the very first time, which required a lot of effort and time. In this research work, 111 different "Chhand" rules were found. This paper presents rulebased modeling of all of the "Chhands". Out of the all modeled "Chhands" the research work covers 53 "Chhands" for which at least 20 to 277 examples were found and used for automatic processing of the data for metadata generation. For this research work, the automatic metadata generator processed 3120 UTF-8 based inputs of 53 Hindi "Chhand" types, achieved 95.02% overall accuracy, and the overall failure rate was 4.98%. The minimum time taken for the processing of "Chhand" for metadata generation was 1.12 seconds, and the maximum was 91.79 seconds.

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“…Recently, researchers have used emotion-based features to classify literature pieces into genres (Kim, Padó & Klinger, 2017) and other types of classes. Thematic delineations are less common, presumably because they involve a more challenging multi-label classification setting, and are very rarely approached using emotional features (Barros, Rodriguez & Ortigosa, 2013;Pal & Patel, 2020).…”

Section: Related Workmentioning

confidence: 99%

Emotion-based Hierarchical Clustering of Romanian Poetry

Lupea¹,

Briciu²,

Bostenaru³

2021

STUD INFORM CONTROL

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Emotions play a central role in both writing and understanding literary works, and poetry is a genre rich in emotional content, vivid imagery and abstract language. This paper proposes a clustering-based approach to unsupervisedly mine emotional patterns in Mihai Eminescu's poetry. Lexicon-based emotion features are used for the clustering algorithm.Resulting clusters are assessed with regard to manually added characteristics of poems in the form of literary themes. There is a partial overlap between affective and thematic content, consistent with literary evaluations of the same works. Computational approaches have the advantage of being objective and replicable, with unsupervised techniques such as clustering representing a valuable tool in the exploration of literary works. Nonetheless, no specific emotional patterns, as determined by the proposed method, can be fully associated with particular literary themes.

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Model for Classification of Poems in Hindi Language Based on Ras

Cited by 18 publications

References 11 publications

A Novel Approach for Identification of Figurative Language Types in Devanagari Scripted Languages

A Novel Approach for Identification of Figurative Language Types in Devanagari Scripted Languages

Stanza Type Identification using Systematization of Versification System of Hindi Poetry

Emotion-based Hierarchical Clustering of Romanian Poetry

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