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Found 26 papers, 6 papers with code
Text Simplification for Comprehension-based Question-Answering
Text simplification is the process of splitting and rephrasing a sentence to a sequence of sentences making it easier to read and understand while preserving the content and approximating the original meaning.
IIITG-ADBU@HASOC-Dravidian-CodeMix-FIRE2020: Offensive Content Detection in Code-Mixed Dravidian Text
The XLM-RoBERTa based classifier performed the best on the code-mixed Tamil text.
IIITG-ADBU at SemEval-2020 Task 8: A Multimodal Approach to Detect Offensive, Sarcastic and Humorous Memes
This architecture utilizes both textual and visual information present in a meme.
IIITG-ADBU at SemEval-2020 Task 9: SVM for Sentiment Analysis of English-Hindi Code-Mixed Text
In this paper, we present the results that the team IIITG-ADBU (codalab username {`}abaruah{'}) obtained in the SentiMix task (Task 9) of the International Workshop on Semantic Evaluation 2020 (SemEval 2020).
KAFK at SemEval-2020 Task 8: Extracting Features from Pre-trained Neural Networks to Classify Internet Memes
This paper presents two approaches for the internet meme classification challenge of SemEval-2020 Task 8 by Team KAFK (cosec).
KAFK at SemEval-2020 Task 12: Checkpoint Ensemble of Transformers for Hate Speech Classification
This paper presents the approach of Team KAFK for the English edition of SemEval-2020 Task 12.
IIITG-ADBU at SemEval-2020 Task 12: Comparison of BERT and BiLSTM in Detecting Offensive Language
The BiLSTM classifier obtained macro F1 score of 0. 57565 for subtask C. The paper also performs an analysis of the errors made by our classifiers.
Bi-ISCA: Bidirectional Inter-Sentence Contextual Attention Mechanism for Detecting Sarcasm in User Generated Noisy Short Text
This paper proposes a new state-of-the-art deep learning architecture that uses a novel Bidirectional Inter-Sentence Contextual Attention mechanism (Bi-ISCA) to capture inter-sentence dependencies for detecting sarcasm in the user-generated short text using only the conversational context.
Context-Aware Sarcasm Detection Using BERT
It was found that including the last utterance in the dialogue along with the response improved the performance of the classifier for the Twitter data set.
Gaze-based Autism Detection for Adolescents and Young Adults using Prosaic Videos
Autism often remains undiagnosed in adolescents and adults.
Aggression Identification in English, Hindi and Bangla Text using BERT, RoBERTa and SVM
In our study, we used English BERT (En-BERT), RoBERTa, DistilRoBERTa, and SVM based classifiers for English language.
Aggression Identification
Misogynistic Aggression Identification
ABARUAH at SemEval-2019 Task 5 : Bi-directional LSTM for Hate Speech Detection
In this paper, we present the results obtained using bi-directional long short-term memory (BiLSTM) with and without attention and Logistic Regression (LR) models for SemEval-2019 Task 5 titled {''}HatEval: Multilingual Detection of Hate Speech Against Immigrants and Women in Twitter{''}.
A Hitchhiker's Guide On Distributed Training of Deep Neural Networks
Deep learning has led to tremendous advancements in the field of Artificial Intelligence.
AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias
Such architectural design and abstractions enable researchers and developers to extend the toolkit with their new algorithms and improvements, and to use it for performance benchmarking.
Automated Test Generation to Detect Individual Discrimination in AI Models
In this paper, we present an automated technique to generate test inputs, which is geared towards finding individual discrimination.
A Survey of Modern Object Detection Literature using Deep Learning
Object detection is the identification of an object in the image along with its localisation and classification.
Topical Stance Detection for Twitter: A Two-Phase LSTM Model Using Attention
The topical stance detection problem addresses detecting the stance of the text content with respect to a given topic: whether the sentiment of the given text content is in FAVOR of (positive), is AGAINST (negative), or is NONE (neutral) towards the given topic.
EmTaggeR: A Word Embedding Based Novel Method for Hashtag Recommendation on Twitter
The top-K hashtags that appear in this ranked list, are recommended for the given test post.
CVBed: Structuring CVs usingWord Embeddings
Automatic analysis of curriculum vitae (CVs) of applicants is of tremendous importance in recruitment scenarios.
Graph Based Sentiment Aggregation using ConceptNet Ontology
The sentiment aggregation problem accounts for analyzing the sentiment of a user towards various aspects/features of a product, and meaningfully assimilating the pragmatic significance of these features/aspects from an opinionated text.
Learning Cognitive Features from Gaze Data for Sentiment and Sarcasm Classification using Convolutional Neural Network
We contend that manual extraction of features may not be the best way to tackle text subtleties that characteristically prevail in complex classification tasks like Sentiment Analysis and Sarcasm Detection, and that even the extraction and choice of features should be delegated to the learning system.
Leveraging Cognitive Features for Sentiment Analysis
Sentiments expressed in user-generated short text and sentences are nuanced by subtleties at lexical, syntactic, semantic and pragmatic levels.
Harnessing Cognitive Features for Sarcasm Detection
In this paper, we propose a novel mechanism for enriching the feature vector, for the task of sarcasm detection, with cognitive features extracted from eye-movement patterns of human readers.
A Paraphrase and Semantic Similarity Detection System for User Generated Short-Text Content on Microblogs
We propose a set of features that, although well-known in the NLP literature for solving other problems, have not been explored for detecting paraphrase or semantic similarity, on noisy user-generated short-text data such as Twitter.
