Bidirectional recurrent neural networks

View Researcher's Other Codes

Disclaimer: The provided code links for this paper are external links. Science Nest has no responsibility for the accuracy, legality or content of these links. Also, by downloading this code(s), you agree to comply with the terms of use as set out by the author(s) of the code(s).

Please contact us in case of a broken link from here

Authors Mike Schuster, K. Paliwal
Journal/Conference Name IEEE Transactions on Signal Processing
Paper Category
Paper Abstract Abstract Poetry is an important component of any language. Much of a nation’s history and culture are documented in poems. A poem has a rhythmic flow which is quite different as compared to a prose. Each language has its own set of rhythmical structures for poems, called meters. Identifying the meters of Arabic poems is a lengthy and complicated process. To classify a poem’s meter, the text of the poem should be encoded in a special Arudi form which needs complex rule-based transformations before another set of rules can be used to finally classify the meters. This paper introduces a novel method for classifying poem meters of Arabic poems using RNN-based deep learning. It bypasses the need to transform the poem to the Arudi form as well as the need to explicitly encode the complex rules that are usually followed to determine the meter. The presented method was evaluated on a large dataset collected specifically for this purpose. We are able to classify the poem meters with an accuracy of 94.32% on an independent test set.
Date of publication 2020
Code Programming Language Jupyter Notebook
Comment

Copyright Researcher 2022