160 citations · 162 across the 6 of their papers we have counts for
7 papers
Neon: News Entity-Interaction Extraction for Enhanced Question Answering
Sneha Singhania, Silviu Cucerzan, Allen Herring +1
Capturing fresh information in near real-time and using it to augment existing large language models (LLMs) is essential to generate up-to-date, grounded, and reliable output. This…
Recall Them All: Retrieval-Augmented Language Models for Long Object List Extraction from Long Documents
Sneha Singhania, Simon Razniewski, Gerhard Weikum
Methods for relation extraction from text mostly focus on high precision, at the cost of limited recall. High recall is crucial, though, to populate long lists of object entities t…
Large Language Models and Knowledge Graphs: Opportunities and Challenges
Jeff Z. Pan, Simon Razniewski, Jan-Christoph Kalo +13
Large Language Models (LLMs) have taken Knowledge Representation -- and the world -- by storm. This inflection point marks a shift from explicit knowledge representation to a renew…
Extracting Multi-valued Relations from Language Models
Sneha Singhania, Simon Razniewski, Gerhard Weikum
The widespread usage of latent language representations via pre-trained language models (LMs) suggests that they are a promising source of structured knowledge. However, existing m…
3HAN: A Deep Neural Network for Fake News Detection
Sneha Singhania, Nigel Fernandez, Shrisha Rao
The rapid spread of fake news is a serious problem calling for AI solutions. We employ a deep learning based automated detector through a three level hierarchical attention network…
Evaluating Language Models for Knowledge Base Completion
Blerta Veseli, Sneha Singhania, Simon Razniewski +1
Structured knowledge bases (KBs) are a foundation of many intelligent applications, yet are notoriously incomplete. Language models (LMs) have recently been proposed for unsupervis…
Predicting Document Coverage for Relation Extraction
Sneha Singhania, Simon Razniewski, Gerhard Weikum
This paper presents a new task of predicting the coverage of a text document for relation extraction (RE): does the document contain many relational tuples for a given entity? Cove…