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.

We perform an open, open-domain test of how the proposed approach compares to a wide range of existing methods. Our goal is to show that the proposed approaches tend to deliver the desired outcome in a low-resource setting. In our test, we present an algorithm for comparing two different tracking and tracking approaches. The algorithms are based on a simple iterative model of two images where the goal is to find the best one. We also provide experiments with two different approaches: a low-resource and a large-resource tracking approach in an open-domain setting. Results on several real-world databases show the superiority of the proposed approaches in terms of accuracy, recall and retrieval.

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On Detecting Similar Languages in Text in Hindi

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    A New Approach to Online Multi-Camera Tracking and TrackingWe perform an open, open-domain test of how the proposed approach compares to a wide range of existing methods. Our goal is to show that the proposed approaches tend to deliver the desired outcome in a low-resource setting. In our test, we present an algorithm for comparing two different tracking and tracking approaches. The algorithms are based on a simple iterative model of two images where the goal is to find the best one. We also provide experiments with two different approaches: a low-resource and a large-resource tracking approach in an open-domain setting. Results on several real-world databases show the superiority of the proposed approaches in terms of accuracy, recall and retrieval.


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