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1
Jibes & Delights: A Dataset of Targeted Insults and Compliments to Tackle Online Abuse​ ...
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2
ViTA: Visual-Linguistic Translation by Aligning Object Tags ...
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3
HASOCOne@FIRE-HASOC2020: Using BERT and Multilingual BERT models for Hate Speech Detection ...
Dowlagar, Suman; Mamidi, Radhika. - : arXiv, 2021
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4
Multilingual Pre-Trained Transformers and Convolutional NN Classification Models for Technical Domain Identification ...
Dowlagar, Suman; Mamidi, Radhika. - : arXiv, 2021
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5
Automatic Learning Assistant in Telugu ...
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6
gundapusunil at SemEval-2020 Task 9: Syntactic Semantic LSTM Architecture for SENTIment Analysis of Code-MIXed Data ...
Gundapu, Sunil; Mamidi, Radhika. - : arXiv, 2020
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7
A SentiWordNet Strategy for Curriculum Learning in Sentiment Analysis ...
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8
Word Level Language Identification in English Telugu Code Mixed Data ...
Gundapu, Sunil; Mamidi, Radhika. - : arXiv, 2020
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9
A Sentiwordnet Strategy for Curriculum Learning in Sentiment Analysis
In: Natural Language Processing and Information Systems (2020)
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10
Conversational implicatures in English dialogue: Annotated dataset ...
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11
BCSAT : A Benchmark Corpus for Sentiment Analysis in Telugu Using Word-level Annotations ...
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12
Automatic Target Recovery for Hindi-English Code Mixed Puns ...
Abstract: In order for our computer systems to be more human-like, with a higher emotional quotient, they need to be able to process and understand intrinsic human language phenomena like humour. In this paper, we consider a subtype of humour - puns, which are a common type of wordplay-based jokes. In particular, we consider code-mixed puns which have become increasingly mainstream on social media, in informal conversations and advertisements and aim to build a system which can automatically identify the pun location and recover the target of such puns. We first study and classify code-mixed puns into two categories namely intra-sentential and intra-word, and then propose a four-step algorithm to recover the pun targets for puns belonging to the intra-sentential category. Our algorithm uses language models, and phonetic similarity-based features to get the desired results. We test our approach on a small set of code-mixed punning advertisements, and observe that our system is successfully able to recover the targets ...
Keyword: Artificial Intelligence cs.AI; Computation and Language cs.CL; FOS Computer and information sciences
URL: https://arxiv.org/abs/1806.04535
https://dx.doi.org/10.48550/arxiv.1806.04535
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13
Towards Automation of Sense-type Identification of Verbs in OntoSenseNet(Telugu) ...
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14
Towards Enhancing Lexical Resource and Using Sense-annotations of OntoSenseNet for Sentiment Analysis ...
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15
Context and Humor: Understanding Amul advertisements of India ...
Mamidi, Radhika. - : arXiv, 2018
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