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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
Abstract: In a multilingual or sociolingual configuration Intra-sentential Code Switching (ICS) or Code Mixing (CM) is frequently observed nowadays. In the world, most of the people know more than one language. CM usage is especially apparent in social media platforms. Moreover, ICS is particularly significant in the context of technology, health, and law where conveying the upcoming developments are difficult in one's native language. In applications like dialog systems, machine translation, semantic parsing, shallow parsing, etc. CM and Code Switching pose serious challenges. To do any further advancement in code-mixed data, the necessary step is Language Identification. In this paper, we present a study of various models - Nave Bayes Classifier, Random Forest Classifier, Conditional Random Field (CRF), and Hidden Markov Model (HMM) for Language Identification in English - Telugu Code Mixed Data. Considering the paucity of resources in code mixed languages, we proposed the CRF model and HMM model for word level ... : 7 pages, 3 figures ...
Keyword: Computation and Language cs.CL; FOS Computer and information sciences
URL: https://dx.doi.org/10.48550/arxiv.2010.04482
https://arxiv.org/abs/2010.04482
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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 ...
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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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