Proceedings ArticleDOI
Kannada Grammar Checker Using LSTM Neural Network
TLDR
A model is advocated that employs a deep learning method to train the LSTM (Long Short Term Memory) neural network trained over a massive data set to fulfill the necessary categorisation, using a context-based retention of the data attained through Word2Vec along with the TensorFlow and Keras packages.Abstract:
Language is the most fundamental and historically normal means of communication today. Grammar plays a critical role in the excellence of a language. As individuals have already been educated throughout our existence with an accumulation of knowledge that is accrued, mastered over time with guidelines and a restriction of significance that allows us to comprehend and interact one another. But also to translate such awareness into a computer, to be capable of interpreting and classifying contextual evidence into a proper syntactical form, thereby validating that the information was in the correct form, is incredibly necessary at the current time since it is a sophisticated activity. The paper addresses the issue and asserts the advancement of such grammar verifying mechanism for the Dravidian language Kannada. Among the first account would be that the intricacy of the language poses a problem and preferring to have a rule based stance is an easier route and makes it possible to identify detected flaws competently. It takes a linguistic specialist to compile hundreds of parallel standards that are difficult to preserve. Here, a model is advocated that employs a deep learning method to train the LSTM (Long Short Term Memory) neural network trained over a massive data set to fulfill the necessary categorisation, using a context-based retention of the data attained through Word2Vec along with the TensorFlow and Keras packages. The proposed system is able to perform Grammatical Error Detection (GED) effectively.read more
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Book ChapterDOI
KDC: New Dataset for Kannada Document Categorization
TL;DR: In this paper , a new dataset for Kannada document classification is presented and the validation results of the new dataset are presented by experimenting the various machine learning algorithms on the dataset.
References
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Distributed Representations of Words and Phrases and their Compositionality
TL;DR: This paper presents a simple method for finding phrases in text, and shows that learning good vector representations for millions of phrases is possible and describes a simple alternative to the hierarchical softmax called negative sampling.
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Distributed Representations of Words and Phrases and their Compositionality
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Word Representations: A Simple and General Method for Semi-Supervised Learning
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