76 citations · 110 across the 6 of their papers we have counts for
7 papers
Explore BiLSTM-CRF-Based Models for Open Relation Extraction
Tao Ni, Qing Wang, Gabriela Ferraro
Extracting multiple relations from text sentences is still a challenge for current Open Relation Extraction (Open RE) tasks. In this paper, we develop several Open RE models based…
Learning to Continually Learn Rapidly from Few and Noisy Data
Nicholas I-Hsien Kuo, Mehrtash Harandi, Nicolas Fourrier +3
Neural networks suffer from catastrophic forgetting and are unable to sequentially learn new tasks without guaranteed stationarity in data distribution. Continual learning could be…
MTL2L: A Context Aware Neural Optimiser
Nicholas I-Hsien Kuo, Mehrtash Harandi, Nicolas Fourrier +3
Learning to learn (L2L) trains a meta-learner to assist the learning of a task-specific base learner. Previously, it was shown that a meta-learner could learn the direct rules to u…
Lightme: Analysing Language in Internet Support Groups for Mental Health
Gabriela Ferraro, Brendan Loo Gee, Shenjia Ji +1
Background: Assisting moderators to triage harmful posts in Internet Support Groups is relevant to ensure its safe use. Automated text classification methods analysing the language…
Transfer Learning for Hate Speech Detection in Social Media
Lanqin Yuan, Tianyu Wang, Gabriela Ferraro +2
Today, the internet is an integral part of our daily lives, enabling people to be more connected than ever before. However, this greater connectivity and access to information incr…
Named Entity Recognition for Novel Types by Transfer Learning
Lizhen Qu, Gabriela Ferraro, Liyuan Zhou +2
In named entity recognition, we often don't have a large in-domain training corpus or a knowledge base with adequate coverage to train a model directly. In this paper, we propose a…
Big Data Small Data, In Domain Out-of Domain, Known Word Unknown Word: The Impact of Word Representation on Sequence Labelling Tasks
Lizhen Qu, Gabriela Ferraro, Liyuan Zhou +3
Word embeddings -- distributed word representations that can be learned from unlabelled data -- have been shown to have high utility in many natural language processing application…