Name: Denis Newman-Griffis
Type: User
Company: University of Sheffield
Bio: Lecturer/Assistant Professor in Data Science at the University of Sheffield Information School. (Past: Ohio State, NIH Clinical Center, Univ Pittsburgh.)
Twitter: drgriffis
Location: Sheffield, UK
Blog: https://denis.newman-griffis.org
Denis Newman-Griffis's Projects
Hands-on tutorial on deep learning with a special focus on Natural Language Processing (NLP)
Scripts for getting BERT embeddings with token-level alignment in HDF5 format
CLI for reading from .ini config files
Python utility for logging experimental configurations, for replicability/reproducibility
Python scripts for processing various NLP corpora
Website/resources for CSE 3521 (Intro to AI) Autumn 2017
Utility scripts and Python code for working with cTAKES output
Wrappers for dealing with various formats of dataset
Website of Denis R Newman-Griffis
Reimplementation of WSD experiments from Peters et al (2018)
A suite of tasks for extrinsic evaluation of word embedding models.
Extension to Geeknote for linking local Markdown files with Evernote notes
GloVe model for distributed word representation
Python logging without the timestamps/log levels. Plus some tweaks.
INF111 AY2023-2024 - Week 12 - Working with Modules example
Analysis of ambiguity in Electronic Health Record datasets for Medical Concept Normalization
Miscellaneous utility code
DNN framework for learning a nonlinear mapping between two sets of embeddings
Named-entity recognition using neural networks. Easy-to-use and state-of-the-art results.
Tools for working with PubMed data.
Python library for interacting with pre-trained word embeddings
Python wrapper for UMLS REST API
Virtual environment for replicating experiments from the paper "A Quantitative and Qualitative Evaluation of Sentence Boundary Detection for the Clinical Domain," appearing at AMIA CRI 2016.
Similarity and relatedness experiments for word embeddings
Stanford Network Analysis Platform (SNAP) is a general purpose network analysis and graph mining library.
Preprocessing and analysis for training SNOMED-CT concept embeddings from CORD-19 corpus