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Found 17 papers, 1 papers with code
Human Centered AI for Indian Legal Text Analytics
Legal research is a crucial task in the practice of law.
xLP: Explainable Link Prediction for Master Data Management
Explaining neural model predictions to users requires creativity.
Infusing Knowledge into Large Language Models with Contextual Prompts
Knowledge infusion is a promising method for enhancing Large Language Models for domain-specific NLP tasks rather than pre-training models over large data from scratch.
Foundation Model Sherpas: Guiding Foundation Models through Knowledge and Reasoning
Foundation models (FMs) such as large language models have revolutionized the field of AI by showing remarkable performance in various tasks.
Automated Answer Validation using Text Similarity
Automated answer validation can help improve learning outcomes by providing appropriate feedback to learners, and by making question answering systems and online learning solutions more widely available.
xEM: Explainable Entity Matching in Customer 360
Entity matching in Customer 360 is the task of determining if multiple records represent the same real world entity.
Similar Cases Recommendation using Legal Knowledge Graphs
A legal knowledge graph constructed from court cases, judgments, laws and other legal documents can enable a number of applications like question answering, document similarity, and search.
Reimagining GNN Explanations with ideas from Tabular Data
Explainability techniques for Graph Neural Networks still have a long way to go compared to explanations available for both neural and decision decision tree-based models trained on tabular data.
Towards Automated Evaluation of Explanations in Graph Neural Networks
Explaining Graph Neural Networks predictions to end users of AI applications in easily understandable terms remains an unsolved problem.
Document Structure aware Relational Graph Convolutional Networks for Ontology Population
Ontologies comprising of concepts, their attributes, and relationships are used in many knowledge based AI systems.
Explainable Link Prediction for Privacy-Preserving Contact Tracing
Contact Tracing has been used to identify people who were in close proximity to those infected with SARS-Cov2 coronavirus.
Link Prediction using Graph Neural Networks for Master Data Management
Learning graph representations of n-ary relational data has a number of real world applications like anti-money laundering, fraud detection, and customer due diligence.
Data Augmentation for Personal Knowledge Base Population
Cold start knowledge base population (KBP) is the problem of populating a knowledge base from unstructured documents.
A Neural Architecture for Person Ontology population
A person ontology comprising concepts, attributes and relationships of people has a number of applications in data protection, didentification, population of knowledge graphs for business intelligence and fraud prevention.
Document Structure Measure for Hypernym discovery
Hypernym discovery is the problem of finding terms that have is-a relationship with a given term.
Fine Grained Classification of Personal Data Entities
Entity Type Classification can be defined as the task of assigning category labels to entity mentions in documents.
Collective Learning From Diverse Datasets for Entity Typing in the Wild
The CLF first creates a unified hierarchical label set (UHLS) and a label mapping by aggregating label information from all available datasets.
