93 citations · 201 across the 20 of their papers we have counts for
22 papers
Winter Wheat Crop Yield Prediction on Multiple Heterogeneous Datasets using Machine Learning
Yogesh Bansal, David Lillis, Mohand Tahar Kechadi
Winter wheat is one of the most important crops in the United Kingdom, and crop yield prediction is essential for the nation's food security. Several studies have employed machine…
A Deep Learning Model for Heterogeneous Dataset Analysis -- Application to Winter Wheat Crop Yield Prediction
Yogesh Bansal, David Lillis, Mohand Tahar Kechadi
Western countries rely heavily on wheat, and yield prediction is crucial. Time-series deep learning models, such as Long Short Term Memory (LSTM), have already been explored and ap…
Enhancing Legal Argument Mining with Domain Pre-training and Neural Networks
Gechuan Zhang, Paul Nulty, David Lillis
The contextual word embedding model, BERT, has proved its ability on downstream tasks with limited quantities of annotated data. BERT and its variants help to reduce the burden of…
UCD-CS at TREC 2021 Incident Streams Track
Congcong Wang, David Lillis
In recent years, the task of mining important information from social media posts during crises has become a focus of research for the purposes of assisting emergency response (ES)…
Crisis Domain Adaptation Using Sequence-to-sequence Transformers
Congcong Wang, Paul Nulty, David Lillis
User-generated content (UGC) on social media can act as a key source of information for emergency responders in crisis situations. However, due to the volume concerned, computation…
Transformer-based Multi-task Learning for Disaster Tweet Categorisation
Congcong Wang, Paul Nulty, David Lillis
Social media has enabled people to circulate information in a timely fashion, thus motivating people to post messages seeking help during crisis situations. These messages can cont…