Chan Hee (Luke) Song송찬희

I am a computer science doctoral student at The Ohio State University. My research focus is NLP, specifically Knowledge Base (KB) construction, interface, and reasoning. I received BS Cum Laude in Computer Science from University of Notre Dame in May 2020. In Notre Dame, I was a part of Notre Dame Natural Language Processing Group.

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  • 08/2020: Joining THE Ohio State University for CS PhD program. I will be focusing on NLP specifically KB construction, interface, and reasoning. Go Bucks!


Currently my interests are Knowledge Base (KB) consturction, interface, and reasoning but also am interested in general information extraction. I have experience in machine translation, word embeddings, NER. Last summer I have been at the HLTCOE working on improving neural Named Entity Recognition systems.


Using Chinese Glyphs for Named Entity Recognition
Chan Hee Song, Arijit Sehanobish
extended abstract, full paper, AAAI-20 student abstract

Used Chinese glyph information to augment neural named entity system. Introduced a convolutional neural network (CNN) architecture that achieves a state-of-art performance on Chinese Weibo NER dataset and sets a new baseline F1 score on Chinese OntoNotes v5.0 dataset. Easy to implement, train and uses less data. Robust to non-Chinese characters in the dataset.


Gazetteer Generation for Neural Named Entity Recognition
Chan Hee Song, Dawn Lawrie, Tim Finin, Jim Mayfield
paper, FLAIRS-33

Introduced a simple method to generate gazetteers from the Web. Generated gazetteer is used as a feature to augment neural named entity recognition system, showing improvement over baseline. Developed a method to create new training data using gazetteer entity replacement. Presented new Russian NER corpus gathered from Reddit.


Sentence Representation for Neural Machine Translation Systems
Chan Hee Song, David Chiang
06/2018 -> 05/2019

Independent undergraduate researcher under supervision of professor David Chiang. I investigated the impact of sentence representation on neural machine translation to develop an optimal encoding mechanism that has a better trade-off between time and accuracy. I am using C, Python and PyTorch library to build encoder/decoder, modify neural machine translation system, and implement new equations.


Verba Volant, Scripta Manent: Automatic Transcription of Medieval Latin Manuscripts
Undergraduate Research Assistant
01/2018 -> 05/2018

Research assistant to the project which aims to find a novel method to process Vatican Secret Archives to transfer early Medieval Latin handwritten papers into a computer readable format. I analyzed various character recognition algorithm and used Python to build an auto analyzer tool for Optical Character Recognition systems

Work Experience

Human Language Technology Center For Excellence, Johns Hopkins University
Visiting NLP/ML Researcher, SCALE program
05/2019 -> 08/2019

I implemented various state-of-the-art neural architectures and adapted to named entity recognition task. Introduced a lexical feature generated from gazetteers to the named entity recognition (NER) system. As an independent project, developed a novel named entity recognition system achieving state-of-the-art F1 score on Chinese datasets using glyph features generated from convolutional neural network.


Kyndi, San Mateo CA
Software Engineering Intern
01/2019 -> 05/2019

I migrated in-memory graph to external database by building a GraphQL server to interact with the external database using Apollo and Typescript. Also improved accuracy and representation of the knowledge graph by writing unique graph operations on a large knowledge graph.


Undergraduate Teaching Assistant, Theory of Computing Spring 2020
Undergraduate Teaching Assistant, Fundamentals of Computing Fall 2018

Updated 05/2020
(borrowed from here)