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

  title={On Exhaustive Evaluation of Eager Machine Learning Algorithms for Classification of Hindi Verses},
  author={Prafulla Bharat Bafna and Jatinderkumar R.},
  journal={International Journal of Advanced Computer Science and Applications},
  • P. Bafna, Jatinderkumar R.
  • Published 2020
  • Computer Science
  • International Journal of Advanced Computer Science and Applications
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… 

Figures and Tables from this paper

An Application of Zipf's Law for Prose and Verse Corpora Neutrality for Hindi and Marathi Languages
Common tokens from corpora of verses and proses of Marathi as well as Hindi are identified to prove that both of them behave same as per as NLP activities are concerened and the betterment of BaSa over Zipf’s law is proved.
Sentiment Analysis in Hindi—A Survey on the State-of-the-art Techniques
This research portrays a systematic review specifically in the field of Hindi SA of state-of-the-art computational intelligence techniques based on different aspects such as their impact on the issues of SA, levels of analysis, and performance evaluation measures.
Towards Natural Language Processing with Figures of Speech in Hindi Poetry
This work is the first of its kind in Hindi Natural Language Processing (NLP), which touches on the area of Hindi figure of speech and has created a systematic hierarchical structure of Hindi “Alankaar” types and sub-types and attempted and extended the work to identify a few.
Stanza Type Identification using Systematization of Versification System of 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, and provides some advanced information and techniques for metadata generation for ”Muktak Chhands”.
Analysing the Poetic Structure of Jana-Gaṇa-Mana in Entirety: A Statistical Approach
Measurable investigation of abstract content so as to bring bits of knowledge into its expressive highlights has been a shared zone of enthusiasm among the aficionados of writing and measurements.


Automatic Punjabi poetry classification using machine learning algorithms with reduced feature set
This work Classification of poems is very challenging in computational linguistic point of view and Naive Bayes outperformed all other classifiers utilising 60% top ranked features and hyperpipes is the least efficient classifier.
Punjabi Poetry Classification: The Test of 10 Machine Learning Algorithms
Results for Punjabi poetry classification revealed that 4 machine learning algorithms namely, Hyperpipes (HP), K- nearest neighbour (KNN), Naive Bayes (NB) and Support Vector Machine (SVM) with an accuracy of 50.63 %, 52.75 % and 58.79 % respectively, outperformed all other machinelearning algorithms under the test.
Hindi Multi-document Word Cloud based Summarization through Unsupervised Learning
  • P. Bafna, Jatinderkumar R. Saini
  • Computer Science
    2019 9th International Conference on Emerging Trends in Engineering and Technology - Signal and Information Processing (ICETET-SIP-19)
  • 2019
The objective is to manage the documents and summarize Hindi corpus by applying extracting tokens and document clustering, an application of TF-IDF, cosine-based document similarity measures and cluster dendrograms, in addition to various other Natural Language Processing (NLP) activities.
Designing punjabi poetry classifiers using machine learning and different textual features
Content-based Punjabi poetry classifier was developed using Weka toolset and revealed that semantic feature performed better as compared to lexical and syntactic features.
Marathi Text Analysis using Unsupervised Learning and Word Cloud
  • Computer Science
    International Journal of Engineering and Advanced Technology
  • 2020
Results prove the robustness of the proposed approach for Marathi Corpus, an application of TF-IDF, cosine-based document similarity measures and cluster dendrograms, in addition to various other Natural Language Processing (NLP) activities.
Question classification using support vector machines
This paper proposes to use a special kernel function called the tree kernel to enable the SVM to take advantage of the syntactic structures of questions, and describes how the tree Kernel can be computed efficiently by dynamic programming.
Computational linguistic prosody rule-based unified technique for automatic metadata generation for Hindi poetry
This research paper majorly focuses on the unified-rule based technique for the generation of metadata based on the different set of rules of prosody, which was able to achieve 98.09% accuracy with the implementation of this unified- rule based technique.
Automatic classification of Punjabi poetries using poetic features
Automatic classification of poetic content is very challenging from the computational linguistic point of view. For library suggestion framework, poetries can be grouped on different measurements,
Model for Classification of Poems in Hindi Language Based on Ras
The developed model will classify poem into Shringar, Hasya, Adbhuta, Shanta, Raudra, Veera, Karuna, Bhayanaka, Vibhasta rasas, which will use mix of part-of-speech-based feature and emotional
Hindi Text Document Classification System Using SVM and Fuzzy: A Survey
A new idea of Hindi printed and handwritten document classification system using support vector machine and fuzzy logic first pre-processes and then classifies textual imaged documents into predefined categories.