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Hindi Poetry Classification using Eager Supervised Machine Learning Algorithms
TLDR
Two eager machine learning algorithms are applied on the corpus containing 450 Hindi poems and poetry/poem gets classified based on terms present in it using a misclassification error.
Document clustering: TF-IDF approach
TLDR
Term Frequency-Inverse Document Frequency algorithm is used along with fuzzy K-means and hierarchical algorithm along with different clusters of the related documents the resulted silhouette coefficient, entropy and F-measure trend are presented to show algorithm behavior for each data set.
An Application of Zipf's Law for Prose and Verse Corpora Neutrality for Hindi and Marathi Languages
TLDR
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.
Hindi Verse Class Predictor using Concept Learning Algorithms
TLDR
In this paper, 565 Hindi poems are classified based on four topics using lazy machine-learning algorithms which are K-nearest neighbours and regression, and K nearset neighbours performs better than Linear regression.
Multi-Step Iterative Algorithm for Feature Selection on Dynamic Documents
TLDR
The authors propose clustering based multistep iterative algorithm that takes advantage of semantic relativity measure between the terms to give improved result on certain set of documents which are well-articulated, such as research papers.
Semantic Clustering Driven Approaches to Recommender Systems
TLDR
Wordnet based Synset grouping approach is presented that not only reduces dimensions in FM but also generates Feature vectors (FV) for each cluster with significantly improved cluster quality.
Identification of Significant Challenges in the Sports Domain using Clustering and Feature Selection Techniques
TLDR
Authors have used clustering technique for optimal challenge selection and cluster validation parameters to prove accuracy of the result.
Task recommender system using semantic clustering to identify the right personnel
TLDR
An automated Task Recommendation System is proposed comprising synset-based feature extraction, iterative semantic clustering and mapping based on semantic similarity that optimizes the candidate selection process by reducing entropy and error and by improving precision and scalability.
On Readability Metrics of Goal Statements of Universities and Brand-Promoting Lexicons for Industries
TLDR
The correlation between the found lexicons and the revenues generated by the considered companies is advocated and Pearson's correlation coefficient and Flesch Readability Index are deployed for the calculation of various metrics to form the basis of the conclusions.
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