Search Results for author:
Found 7 papers, 4 papers with code
Generating Faithful Synthetic Data with Large Language Models: A Case Study in Computational Social Science
Large Language Models (LLMs) have democratized synthetic data generation, which in turn has the potential to simplify and broaden a wide gamut of NLP tasks.
Quote Erat Demonstrandum: A Web Interface for Exploring the Quotebank Corpus
The use of attributed quotes is the most direct and least filtered pathway of information propagation in news.
Strong Heuristics for Named Entity Linking
Named entity linking (NEL) in news is a challenging endeavour due to the frequency of unseen and emerging entities, which necessitates the use of unsupervised or zero-shot methods.
Efficient Entity Candidate Generation for Low-Resource Languages
We also propose a light-weight and simple solution based on the construction of indexes whose design is motivated by more complex transfer learning based neural approaches.
A Critical Re-evaluation of Neural Methods for Entity Alignment
Our findings shed light on the potential problems resulting from an impulsive application of neural methods as a panacea for all data analytics tasks.
Low-Rank Subspaces for Unsupervised Entity Linking
We propose a light-weight and scalable entity linking method, Eigenthemes, that relies solely on the availability of entity names and a referent knowledge base.
Holistic Influence Maximization: Combining Scalability and Efficiency with Opinion-Aware Models
In this paper, we propose a holistic solution to the influence maximization (IM) problem.
Social and Information Networks Databases H.2.8
