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Found 13 papers, 7 papers with code
Designing Decision Support Systems for Emergency Response: Challenges and Opportunities
Designing effective emergency response management (ERM) systems to respond to incidents such as road accidents is a major problem faced by communities.
Practitioner-Centric Approach for Early Incident Detection Using Crowdsourced Data for Emergency Services
First, we show how crowdsourced reports, ground-truth historical data, and other relevant determinants such as traffic and weather can be used together in a Convolutional Neural Network (CNN) architecture for early detection of emergency incidents.
Cross-Lingual Text Classification of Transliterated Hindi and Malayalam
Transliteration is very common on social media, but transliterated text is not adequately handled by modern neural models for various NLP tasks.
Multilingual Code-Switching for Zero-Shot Cross-Lingual Intent Prediction and Slot Filling
Predicting user intent and detecting the corresponding slots from text are two key problems in Natural Language Understanding (NLU).
Emergency Incident Detection from Crowdsourced Waze Data using Bayesian Information Fusion
Crowdsourcing platforms such as Waze provides an opportunity to develop a rapid, `proactive' approach to collect data about incidents through crowd-generated observational reports.
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Traffic Accident Detection
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Common-Knowledge Concept Recognition for SEVA
We build a common-knowledge concept recognition system for a Systems Engineer's Virtual Assistant (SEVA) which can be used for downstream tasks such as relation extraction, knowledge graph construction, and question-answering.
Unsupervised and Interpretable Domain Adaptation to Rapidly Filter Tweets for Emergency Services
As deep networks struggle with sparse datasets, we show that this can be improved by sharing a base layer for multi-task learning and domain adversarial training.
Diversity-Based Generalization for Unsupervised Text Classification under Domain Shift
At present, the state-of-the-art unsupervised domain adaptation approaches for subjective text classification problems leverage unlabeled target data along with labeled source data.
Evaluating Semantic Feature Representations to Efficiently Detect Hate Intent on Social Media
We hypothesize that semantic features can help enrich the context representation of word senses in a social media post for machine learning algorithms.
Multi-stage Deep Classifier Cascades for Open World Recognition
At present, object recognition studies are mostly conducted in a closed lab setting with classes in test phase typically in training phase.
Modeling Human Annotation Errors to Design Bias-Aware Systems for Social Stream Processing
High-quality human annotations are necessary to create effective machine learning systems for social media.
Enhancing Cohesion and Coherence of Fake Text to Improve Believability for Deceiving Cyber Attackers
We can generate fake text documents by obscuring the salient information in legit text documents.
Real-Time Inference of User Types to Assist with More Inclusive Social Media Activism Campaigns
A generic framework is devised to classify a Twitter user into three classes: organization, male and female in a real-time manner.
