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Found 45 papers, 5 papers with code
IISERB Brains at SemEval-2022 Task 6: A Deep-learning Framework to Identify Intended Sarcasm in English
This paper describes the system architectures and the models submitted by our team “IISERB Brains” to SemEval 2022 Task 6 competition.
Queer In AI: A Case Study in Community-Led Participatory AI
We present Queer in AI as a case study for community-led participatory design in AI.
Applications and Challenges of Sentiment Analysis in Real-life Scenarios
Sentiment analysis has benefited from the availability of lexicons and benchmark datasets created over decades of research.
Striking a Balance: Alleviating Inconsistency in Pre-trained Models for Symmetric Classification Tasks
While fine-tuning pre-trained models for downstream classification is the conventional paradigm in NLP, often task-specific nuances may not get captured in the resultant models.
IISERB Brains at SemEval 2022 Task 6: A Deep-learning Framework to Identify Intended Sarcasm in English
This paper describes the system architectures and the models submitted by our team "IISERBBrains" to SemEval 2022 Task 6 competition.
Recommendation Chart of Domains for Cross-Domain Sentiment Analysis: Findings of A 20 Domain Study
Cross-domain sentiment analysis (CDSA) helps to address the problem of data scarcity in scenarios where labelled data for a domain (known as the target domain) is unavailable or insufficient.
Recommendation Chart of Domains for Cross-Domain Sentiment Analysis:Findings of A 20 Domain Study
Cross-domain sentiment analysis (CDSA) helps to address the problem of data scarcity in scenarios where labelled data for a domain (known as the target domain) is unavailable or insufficient.
Scalability in Perception for Autonomous Driving: Waymo Open Dataset
In an effort to help align the research community's contributions with real-world self-driving problems, we introduce a new large scale, high quality, diverse dataset.
Figurative Usage Detection of Symptom Words to Improve Personal Health Mention Detection
The introduction of figurative usage detection results in an average improvement of 2. 21% F-score of personal health mention detection, in the case of the feature augmentation-based approach.
A Comparison of Word-based and Context-based Representations for Classification Problems in Health Informatics
Distributed representations of text can be used as features when training a statistical classifier.
``When Numbers Matter!'': Detecting Sarcasm in Numerical Portions of Text
Initially, to get an insight into the problem, we implement a rule-based and a statistical machine learning-based (ML) classifier.
Red-faced ROUGE: Examining the Suitability of ROUGE for Opinion Summary Evaluation
Opinion summarisation requires to correctly pair two types of semantic information: (1) aspect or opinion target; and (2) polarity of candidate and reference summaries.
Overview of the 2019 ALTA Shared Task: Sarcasm Target Identification
We present an overview of the 2019 ALTA shared task.
Does Multi-Task Learning Always Help?: An Evaluation on Health Informatics
Multi-Task Learning (MTL) has been an attractive approach to deal with limited labeled datasets or leverage related tasks, for a variety of NLP problems.
Survey of Text-based Epidemic Intelligence: A Computational Linguistic Perspective
Epidemic intelligence deals with the detection of disease outbreaks using formal (such as hospital records) and informal sources (such as user-generated text on the web) of information.
Hate Speech Detection from Code-mixed Hindi-English Tweets Using Deep Learning Models
This paper reports an increment to the state-of-the-art in hate speech detection for English-Hindi code-mixed tweets.
Shot Or Not: Comparison of NLP Approaches for Vaccination Behaviour Detection
Vaccination behaviour detection deals with predicting whether or not a person received/was about to receive a vaccine.
Computational Sarcasm
In case of each of these algorithms, we refer to our work on sarcasm detection and share our learnings.
Detecting Sarcasm Using Different Forms Of Incongruity
Sarcasm is a form of verbal irony that is intended to express contempt or ridicule.
Expect the unexpected: Harnessing Sentence Completion for Sarcasm Detection
However, since the exact word where such an incongruity occurs may not be known in advance, we present two approaches: an All-words approach (which consults sentence completion for every content word) and an Incongruous words-only approach (which consults sentence completion for the 50% most incongruous content words).
`Who would have thought of that!': A Hierarchical Topic Model for Extraction of Sarcasm-prevalent Topics and Sarcasm Detection
Designed on the basis of the intuition that sarcastic tweets are likely to have a mixture of words of both sentiments as against tweets with literal sentiment (either positive or negative), our hierarchical topic model discovers sarcasm-prevalent topics and topic-level sentiment.
Towards Sub-Word Level Compositions for Sentiment Analysis of Hindi-English Code Mixed Text
We introduce a Hindi-English (Hi-En) code-mixed dataset for sentiment analysis and perform empirical analysis comparing the suitability and performance of various state-of-the-art SA methods in social media.
Automatic Identification of Sarcasm Target: An Introductory Approach
To compare our approach, we use two baselines: a na\"ive baseline and another baseline based on work in sentiment target identification.
A Computational Approach to Automatic Prediction of Drunk Texting
Alcohol abuse may lead to unsociable behavior such as crime, drunk driving, or privacy leaks.
Are Word Embedding-based Features Useful for Sarcasm Detection?
For example, this augmentation results in an improvement in F-score of around 4\% for three out of these four feature sets, and a minor degradation in case of the fourth, when Word2Vec embeddings are used.
That'll Do Fine!: A Coarse Lexical Resource for English-Hindi MT, Using Polylingual Topic Models
As demonstrated by the quality of our coarse lexical resource and its benefit to MT, we believe that our sentential approach to create such a resource will help MT for resource-constrained languages.
Automatic Sarcasm Detection: A Survey
Automatic sarcasm detection is the task of predicting sarcasm in text.
Language Recognition using Random Indexing
Random Indexing is a simple implementation of Random Projections with a wide range of applications.
Cost and Benefit of Using WordNet Senses for Sentiment Analysis
However, a moot question is ''''''``is the accuracy improvement commensurate with the cost incurred in annotation''''''''?
