Search Results for author:
Found 4 papers, 1 papers with code
mgsohrab at WNUT 2020 Shared Task-1: Neural Exhaustive Approach for Entity and Relation Recognition Over Wet Lab Protocols
In relation extraction task, we achieved 80. 46% in terms of F-score as the top system in the relation extraction or recognition task.
Ranked #1 on
Named Entity Recognition (NER)
on WNUT 2020
BENNERD: A Neural Named Entity Linking System for COVID-19
We present a biomedical entity linking (EL) system BENNERD that detects named enti- ties in text and links them to the unified medical language system (UMLS) knowledge base (KB) entries to facilitate the corona virus disease 2019 (COVID-19) research.
A Neural Pipeline Approach for the PharmaCoNER Shared Task using Contextual Exhaustive Models
We present a neural pipeline approach that performs named entity recognition (NER) and concept indexing (CI), which links them to concept unique identifiers (CUIs) in a knowledge base, for the PharmaCoNER shared task on pharmaceutical drugs and chemical entities.
Deep Exhaustive Model for Nested Named Entity Recognition
We propose a simple deep neural model for nested named entity recognition (NER).
