11 citations · 23 across the 10 of their papers we have counts for
11 papers
Loss-Based Active Learning for Neural Abstractive Summarization
Michail Ioannou, Tatiana Passali, George Michalopoulos +1
Fine-tuning abstractive summarization models requires high-quality annotated data. However, obtaining such corpora is expensive and time-consuming, as it requires human annotators…
Empowering Healthcare Practitioners with Language Models: Structuring Speech Transcripts in Two Real-World Clinical Applications
Jean-Philippe Corbeil, Asma Ben Abacha, George Michalopoulos +12
Large language models (LLMs) such as GPT-4o and o1 have demonstrated strong performance on clinical natural language processing (NLP) tasks across multiple medical benchmarks. None…
LMR-BENCH: Evaluating LLM Agent's Ability on Reproducing Language Modeling Research
Shuo Yan, Ruochen Li, Ziming Luo +11
Large language model (LLM) agents have demonstrated remarkable potential in advancing scientific discovery. However, their capability in the fundamental yet crucial task of reprodu…
An Investigation of Evaluation Metrics for Automated Medical Note Generation
Asma Ben Abacha, Wen-wai Yim, George Michalopoulos +1
Recent studies on automatic note generation have shown that doctors can save significant amounts of time when using automatic clinical note generation (Knoll et al., 2022). Summari…
ICDBigBird: A Contextual Embedding Model for ICD Code Classification
George Michalopoulos, Michal Malyska, Nicola Sahar +2
The International Classification of Diseases (ICD) system is the international standard for classifying diseases and procedures during a healthcare encounter and is widely used for…
Cohort Characteristics and Factors Associated with Cannabis Use among Adolescents in Canada Using Pattern Discovery and Disentanglement Method
Peiyuan Zhou, Andrew K. C. Wong, Yang Yang +5
COMPASS is a longitudinal, prospective cohort study collecting data annually from students attending high school in jurisdictions across Canada. We aimed to discover significant fr…
LexSubCon: Integrating Knowledge from Lexical Resources into Contextual Embeddings for Lexical Substitution
George Michalopoulos, Ian McKillop, Alexander Wong +1
Lexical substitution is the task of generating meaningful substitutes for a word in a given textual context. Contextual word embedding models have achieved state-of-the-art results…
Where's the Question? A Multi-channel Deep Convolutional Neural Network for Question Identification in Textual Data
George Michalopoulos, Helen Chen, Alexander Wong
In most clinical practice settings, there is no rigorous reviewing of the clinical documentation, resulting in inaccurate information captured in the patient medical records. The g…
UmlsBERT: Clinical Domain Knowledge Augmentation of Contextual Embeddings Using the Unified Medical Language System Metathesaurus
George Michalopoulos, Yuanxin Wang, Hussam Kaka +2
Contextual word embedding models, such as BioBERT and Bio_ClinicalBERT, have achieved state-of-the-art results in biomedical natural language processing tasks by focusing their pre…
Record fusion: A learning approach
Alireza Heidari, George Michalopoulos, Shrinu Kushagra +2
Record fusion is the task of aggregating multiple records that correspond to the same real-world entity in a database. We can view record fusion as a machine learning problem where…
Engineering Oracles for Time-Dependent Road Networks
Spyros Kontogiannis, George Michalopoulos, Georgia Papastavrou +3
We implement and experimentally evaluate landmark-based oracles for min-cost paths in large-scale time-dependent road networks. We exploit parallelism and lossless compression, com…