Franck is a researcher at Adobe Research in San Jose. He received his PhD in machine learning from MIT. His research interests include neural networks and natural language processing. He has worked on several NLP applications such as text classification, sequential labeling, named-entity recognition, question-answering systems, summarization, dialog systems, image editing by voice, and speech recognition.
For more details and links to papers:
– Google Scholar
– Personal website
Strong NLP PhD students are welcome to contact him at dernonco@adobe.com regarding internships anytime in the year. Internships typically result in publications and patents. Collaborations often continue after the internship.
A Joint Model for Definition Extraction with Syntactic Connection and Semantic Consistency
Multi-view Consistency for Relation Extraction via Mutual Information and Structure Prediction
A Markov Network Model for Natural Language Semantic Matching
A Compare-Aggregate Model with Latent Clustering for Answer Selection
Bayesian Optimization for Selecting Efficient Machine Learning Models
Analyzing Sentence Fusion in Abstractive Summarization
On the Effectiveness of the Pooling Methods for Biomedical Relation Extraction with Deep Learning
Margin Call: an Accessible Web-based Text Viewer with Generated Paragraph Summaries in the Margin
Learning Emphasis Selection for Written Text in Visual Media from Crowd-Sourced Label Distributions
Scoring Sentence Singletons and Pairs for Abstractive Summarization
DEFT: A corpus for definition extraction in free-and semi-structured text
Modeling Visual Relationships in Language
Improving Human Text Comprehension through Semi-Markov CRF-based Neural Section Title Generation
A Comparison Study of Human Evaluated Automated Highlighting Systems
A Framework for Speech Recognition Benchmarking
A Web-based Framework for Collecting and Assessing Highlighted Sentences in a Document
MIT-MEDG at SemEval-2018 Task 7: Semantic Relation Classification via Convolutional Neural Network
A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents
A Repository of Corpora for Summarization
Transfer Learning for Named-Entity Recognition with Neural Networks
Behind the scenes: A medical natural language processing project
PubMed 200k RCT: a Dataset for Sequential Sentence Classification in Medical Abstracts
NeuroNER: an easy-to-use program for named-entity recognition based on neural networks
MIT at SemEval-2017 Task 10: Relation Extraction with Convolutional Neural Networks
Neural Networks for Joint Sentence Classification in Medical Paper Abstracts
De-identification of patient notes with recurrent neural networks
Optimizing Neural Network Hyperparameters with Gaussian Processes for Dialog Act Classification
Feature-Augmented Neural Networks for Patient Note De-identification
Improving patient cohort identification using natural language processing
Data Preparation for Secondary Analysis of Electronic Health Records
Sequential Short-Text Classification with Recurrent and Convolutional Neural Networks
Spoken Language Understanding for DSTC 4
Robust Dialog State Tracking for Large Ontologies