Computational linguistic prosody rule-based unified technique for automatic metadata generation for Hindi poetry

Audichya, Milind Kumar; Saini, Jatinderkumar R.

doi:10.1109/icait47043.2019.8987239

Cited by 14 publications

(9 citation statements)

References 6 publications

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“…Results of the methods are input to Naïve Bayes classifiers and its different variations. [15][16][17][18]. The proposed algorithm works well in case of text classification as compared with other existing algorithms.…”

Section: Bakgroundmentioning

confidence: 86%

On Exhaustive Evaluation of Eager Machine Learning Algorithms for Classification of Hindi Verses

Bafna¹,

Jatinderkumar²

2020

IJACSA

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Implementing supervised machine learning on the Hindi corpus for classification and prediction of verses is an untouched and useful area. Classifying and predictions benefits many applications like organizing a large corpus, information retrieval and so on. The metalinguistic facility provided by websites makes Hindi as a major language in the digital domain of information technology today. Text classification algorithms along with Natural Language Processing (NLP) facilitates fast, cost-effective, and scalable solution. Performance evaluation of these predictors is a challenging task. To reduce manual efforts and time spent for reading the document, classification of text data is important. In this paper, 697 Hindi poems are classified based on four topics using four eager machine-learning algorithms. In the absence of any other technique, which achieves prediction on Hindi corpus, misclassification error is used and compared to prove the betterment of the technique. Support vector machine performs best amongst all.

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Section: Bakgroundmentioning

confidence: 86%

On Exhaustive Evaluation of Eager Machine Learning Algorithms for Classification of Hindi Verses

Bafna¹,

Jatinderkumar²

2020

IJACSA

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“…Moreover, it was found that different research work focusing on the various aspects of problems in Indian language-based studies. Audichya and Saini [7] introduced a way through computational linguistics approach for the automatic metadata generation based on the unified rule-based technique for Hindi poetries. They achieved nearly 98% of correct results.…”

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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“…Sanskrit has been explored through stop word generation [34] and analysis [35], bilingual dictionary [36], constituency mapper [37], lemmatizer development [38] and comparison of its morphological analyzers [39]. Hindi text analysis of poetry was presented through an automated system for generation of metadata [40]. Extracting an original word from token is termed as stemming.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Additionally, this operation reduces the number of terms in an information retrieval system, thus decreasing the size of the index files. Different stemming algorithms for non-Indian and Indian language, methods of stemming, accuracy, and errors are explained [3]. Authors have created intelligent system to get desired documents in Marathi language.…”

Section: Literature Reviewmentioning

confidence: 99%

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Marathi Text Analysis using Unsupervised Learning and Word Cloud

Bafna¹,

Saini²

2020

IJEAT

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Managing a large number of textual documents is a critical and significant task and supports many applications ranging from information retrieval to clustering search engine results. Marathi is one of the oldest of the regional languages in the Indo-Aryan language family, dating from about AD 1000. Abundance of Marathi literature has generated a big corpus and need of summarization of information. The objective of this study is to overcome the scalability problem while managing the documents and summarize the Marathi corpus by extracting tokens. The work is better in terms of scalability and supports the consistent quality of cluster for incremental data set. Most of the past and contemporary research works have targeted English corpus document management. Marathi corpus has been mostly exploited by the researchers for exploring stemming, single-document summarization and classifier design on Marathi corpus. Implementing unsupervised learning on the Marathi corpus for summarization of multiple documents through Word Cloud is still an untouched area. Technically speaking, the current work is an application of TF-IDF, cosine-based document similarity measures and cluster dendrograms, in addition to various other Natural Language Processing (NLP) activities. Entropy and precision are used to evaluate the experiments carried on different datasets and results prove the robustness of the proposed approach for Marathi Corpus.

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Computational linguistic prosody rule-based unified technique for automatic metadata generation for Hindi poetry

Cited by 14 publications

References 6 publications

On Exhaustive Evaluation of Eager Machine Learning Algorithms for Classification of Hindi Verses

On Exhaustive Evaluation of Eager Machine Learning Algorithms for Classification of Hindi Verses

Stanza Type Identification using Systematization of Versification System of Hindi Poetry

Marathi Text Analysis using Unsupervised Learning and Word Cloud

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