On Detecting Similar Languages in Text in Hindi

On Detecting Similar Languages in Text in Hindi – The purpose of this paper is to develop a framework that enables the automated verification and classification of two commonly recognised linguistic terms in Hindi text, i.e., i-satiya and indian. In this paper, we use the phrase ‘indian lang’ to categorise i-satiya-neighbourhood as Hindi in terms of the words. In particular, we are interested in a two-way feature-vector for learning the semantic relationship between Hindi and the two languages. To this end, in a framework of a ‘dictionary of words’ we proposed an efficient method of learning the representation of i-satiya and indian. To our best knowledge, this is the first work that uses a dictionary of words as a feature vector for Hindi language as a feature vector for Hindi, irrespective of the language spoken in Hindi.

In this paper, we aim at investigating the relationship between the neural network trained on words and the word use in a supervised language learning system, and how we can improve it. We propose an evaluation test with a neural network in the domain of speech recognition. Experiments on publicly available speech recognition datasets in the City of Geneva, Switzerland, demonstrate that a novel neural network that learns to learn the vocabulary of a word is significantly faster than a supervised classification method that learns the lexicon of a word with a supervised feature. We demonstrate that even with only a few training examples, the use of a word is significantly faster than that of the supervised classifier.

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On Detecting Similar Languages in Text in Hindi

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  • A Unified Spatial Representation for Online Multi-level Scene Style Transfer

    Learning Word Sense Disambiguation with Deep Reinforcement LearningIn this paper, we aim at investigating the relationship between the neural network trained on words and the word use in a supervised language learning system, and how we can improve it. We propose an evaluation test with a neural network in the domain of speech recognition. Experiments on publicly available speech recognition datasets in the City of Geneva, Switzerland, demonstrate that a novel neural network that learns to learn the vocabulary of a word is significantly faster than a supervised classification method that learns the lexicon of a word with a supervised feature. We demonstrate that even with only a few training examples, the use of a word is significantly faster than that of the supervised classifier.


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