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pro vyhledávání: '"Patro, Jasabanta"'
In this paper, we reported our experiments with various strategies to improve code-mixed humour and sarcasm detection. We did all of our experiments for Hindi-English code-mixed scenario, as we have the linguistic expertise for the same. We experimen
Externí odkaz:
http://arxiv.org/abs/2412.12761
Hate detection has long been a challenging task for the NLP community. The task becomes complex in a code-mixed environment because the models must understand the context and the hate expressed through language alteration. Compared to the monolingual
Externí odkaz:
http://arxiv.org/abs/2405.20755
This paper describes the system architectures and the models submitted by our team "IISERBBrains" to SemEval 2022 Task 6 competition. We contested for all three sub-tasks floated for the English dataset. On the leader-board, wegot19th rank out of43 t
Externí odkaz:
http://arxiv.org/abs/2203.02244
In this paper we demonstrate how code-switching patterns can be utilised to improve various downstream NLP applications. In particular, we encode different switching features to improve humour, sarcasm and hate speech detection tasks. We believe that
Externí odkaz:
http://arxiv.org/abs/2005.02295
Autor:
Patro, Jasabanta, Baruah, Sabyasachee, Gupta, Vivek, Choudhury, Monojit, Goyal, Pawan, Mukherjee, Animesh
In this paper, we consider a dataset comprising press releases about health research from different universities in the UK along with a corresponding set of news articles. First, we do an exploratory analysis to understand how the basic information p
Externí odkaz:
http://arxiv.org/abs/1811.07853
Follower count is a factor that quantifies the popularity of celebrities. It is a reflection of their power, prestige and overall social reach. In this paper we investigate whether the social connectivity or the language choice is more correlated to
Externí odkaz:
http://arxiv.org/abs/1811.07169
Autor:
Patro, Jasabanta, Samanta, Bidisha, Singh, Saurabh, Basu, Abhipsa, Mukherjee, Prithwish, Choudhury, Monojit, Mukherjee, Animesh
In this paper, we present a set of computational methods to identify the likeliness of a word being borrowed, based on the signals from social media. In terms of Spearman correlation coefficient values, our methods perform more than two times better
Externí odkaz:
http://arxiv.org/abs/1707.08446
Is this word borrowed? An automatic approach to quantify the likeliness of borrowing in social media
Autor:
Patro, Jasabanta, Samanta, Bidisha, Singh, Saurabh, Mukherjee, Prithwish, Choudhury, Monojit, Mukherjee, Animesh
Code-mixing or code-switching are the effortless phenomena of natural switching between two or more languages in a single conversation. Use of a foreign word in a language; however, does not necessarily mean that the speaker is code-switching because
Externí odkaz:
http://arxiv.org/abs/1703.05122
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