57 citations · 422 across the 74 of their papers we have counts for
6 papers · 1 filter
Leveraging Semantic Type Dependencies for Clinical Named Entity Recognition
Linh Le, Guido Zuccon, Gianluca Demartini +2
Previous work on clinical relation extraction from free-text sentences leveraged information about semantic types from clinical knowledge bases as a part of entity representations.…
Dr ChatGPT, tell me what I want to hear: How prompt knowledge impacts health answer correctness
Guido Zuccon, Bevan Koopman
Generative pre-trained language models (GPLMs) like ChatGPT encode in the model's parameters knowledge the models observe during the pre-training phase. This knowledge is then used…
Dependency-aware Self-training for Entity Alignment
Bing Liu, Tiancheng Lan, Wen Hua +1
Entity Alignment (EA), which aims to detect entity mappings (i.e. equivalent entity pairs) in different Knowledge Graphs (KGs), is critical for KG fusion. Neural EA methods dominat…
Guiding Neural Entity Alignment with Compatibility
Bing Liu, Harrisen Scells, Wen Hua +3
Entity Alignment (EA) aims to find equivalent entities between two Knowledge Graphs (KGs). While numerous neural EA models have been devised, they are mainly learned using labelled…
ActiveEA: Active Learning for Neural Entity Alignment
Bing Liu, Harrisen Scells, Guido Zuccon +2
Entity Alignment (EA) aims to match equivalent entities across different Knowledge Graphs (KGs) and is an essential step of KG fusion. Current mainstream methods -- neural EA model…
The Benefits of Word Embeddings Features for Active Learning in Clinical Information Extraction
Mahnoosh Kholghi, Lance De Vine, Laurianne Sitbon +2
This study investigates the use of unsupervised word embeddings and sequence features for sample representation in an active learning framework built to extract clinical concepts f…