89 citations · 150 across the 11 of their papers we have counts for
12 papers
Narrative Studio: Visual narrative exploration using LLMs and Monte Carlo Tree Search
Parsa Ghaffari, Chris Hokamp
Interactive storytelling benefits from planning and exploring multiple 'what if' scenarios. Modern LLMs are useful tools for ideation and exploration, but current chat-based user i…
STAGE: Simplified Text-Attributed Graph Embeddings Using Pre-trained LLMs
Aaron Zolnai-Lucas, Jack Boylan, Chris Hokamp +1
We present Simplified Text-Attributed Graph Embeddings (STAGE), a straightforward yet effective method for enhancing node features in Graph Neural Network (GNN) models that encode…
KGValidator: A Framework for Automatic Validation of Knowledge Graph Construction
Jack Boylan, Shashank Mangla, Dominic Thorn +3
This study explores the use of Large Language Models (LLMs) for automatic evaluation of knowledge graph (KG) completion models. Historically, validating information in KGs has been…
News Signals: An NLP Library for Text and Time Series
Chris Hokamp, Demian Gholipour Ghalandari, Parsa Ghaffari
We present an open-source Python library for building and using datasets where inputs are clusters of textual data, and outputs are sequences of real values representing one or mor…
360° Stance Detection
Sebastian Ruder, John Glover, Afshin Mehrabani +1
The proliferation of fake news and filter bubbles makes it increasingly difficult to form an unbiased, balanced opinion towards a topic. To ameliorate this, we propose 360° Stance…
Data Selection Strategies for Multi-Domain Sentiment Analysis
Sebastian Ruder, Parsa Ghaffari, John G. Breslin
Domain adaptation is important in sentiment analysis as sentiment-indicating words vary between domains. Recently, multi-domain adaptation has become more pervasive, but existing a…
Knowledge Adaptation: Teaching to Adapt
Sebastian Ruder, Parsa Ghaffari, John G. Breslin
Domain adaptation is crucial in many real-world applications where the distribution of the training data differs from the distribution of the test data. Previous Deep Learning-base…
Towards a continuous modeling of natural language domains
Sebastian Ruder, Parsa Ghaffari, John G. Breslin
Humans continuously adapt their style and language to a variety of domains. However, a reliable definition of `domain' has eluded researchers thus far. Additionally, the notion of…
INSIGHT-1 at SemEval-2016 Task 5: Deep Learning for Multilingual Aspect-based Sentiment Analysis
Sebastian Ruder, Parsa Ghaffari, John G. Breslin
This paper describes our deep learning-based approach to multilingual aspect-based sentiment analysis as part of SemEval 2016 Task 5. We use a convolutional neural network (CNN) fo…
Character-level and Multi-channel Convolutional Neural Networks for Large-scale Authorship Attribution
Sebastian Ruder, Parsa Ghaffari, John G. Breslin
Convolutional neural networks (CNNs) have demonstrated superior capability for extracting information from raw signals in computer vision. Recently, character-level and multi-chann…
INSIGHT-1 at SemEval-2016 Task 4: Convolutional Neural Networks for Sentiment Classification and Quantification
Sebastian Ruder, Parsa Ghaffari, John G. Breslin
This paper describes our deep learning-based approach to sentiment analysis in Twitter as part of SemEval-2016 Task 4. We use a convolutional neural network to determine sentiment…
A Hierarchical Model of Reviews for Aspect-based Sentiment Analysis
Sebastian Ruder, Parsa Ghaffari, John G. Breslin
Opinion mining from customer reviews has become pervasive in recent years. Sentences in reviews, however, are usually classified independently, even though they form part of a revi…