16 citations · 25 across the 7 of their papers we have counts for
9 papers
Transcriptomics-based matching of drugs to diseases with deep learning
Yannis Papanikolaou, Francesco Tuveri, Misa Ogura +1
In this work we present a deep learning approach to conduct hypothesis-free, transcriptomics-based matching of drugs for diseases. Our proposed neural network architecture is train…
Slot Filling for Biomedical Information Extraction
Yannis Papanikolaou, Marlene Staib, Justin Grace +1
Information Extraction (IE) from text refers to the task of extracting structured knowledge from unstructured text. The task typically consists of a series of sub-tasks such as Nam…
Teach me how to Label: Labeling Functions from Natural Language with Text-to-text Transformers
Yannis Papanikolaou
Annotated data has become the most important bottleneck in training accurate machine learning models, especially for areas that require domain expertise. A recent approach to deal…
DARE: Data Augmented Relation Extraction with GPT-2
Yannis Papanikolaou, Andrea Pierleoni
Real-world Relation Extraction (RE) tasks are challenging to deal with, either due to limited training data or class imbalance issues. In this work, we present Data Augmented Relat…
Deep Bidirectional Transformers for Relation Extraction without Supervision
Yannis Papanikolaou, Ian Roberts, Andrea Pierleoni
We present a novel framework to deal with relation extraction tasks in cases where there is complete lack of supervision, either in the form of gold annotations, or relations from…
Neural Embedding Allocation: Distributed Representations of Topic Models
Kamrun Naher Keya, Yannis Papanikolaou, James R. Foulds
Word embedding models such as the skip-gram learn vector representations of words' semantic relationships, and document embedding models learn similar representations for documents…