Search Results for author:
Found 8 papers, 1 papers with code
An Ontology-driven Treatment Article Retrieval System for Precision Oncology
This paper presents an ontology-driven treatment article retrieval system developed and experimented using the data and ground truths provided by the TREC 2017 precision medicine track.
DR-BiLSTM: Dependent Reading Bidirectional LSTM for Natural Language Inference
Instead, we propose a novel dependent reading bidirectional LSTM network (DR-BiLSTM) to efficiently model the relationship between a premise and a hypothesis during encoding and inference.
Ranked #16 on
Natural Language Inference
on SNLI
Learning to Diagnose: Assimilating Clinical Narratives using Deep Reinforcement Learning
Clinical diagnosis is a critical and non-trivial aspect of patient care which often requires significant medical research and investigation based on an underlying clinical scenario.
Condensed Memory Networks for Clinical Diagnostic Inferencing
We introduce condensed memory neural networks (C-MemNNs), a novel model with iterative condensation of memory representations that preserves the hierarchy of features in the memory.
Neural Clinical Paraphrase Generation with Attention
Paraphrase generation is important in various applications such as search, summarization, and question answering due to its ability to generate textual alternatives while keeping the overall meaning intact.
Neural Paraphrase Generation with Stacked Residual LSTM Networks
To the best of our knowledge, this work is the first to explore deep learning models for paraphrase generation.
A Data-Driven Approach for Semantic Role Labeling from Induced Grammar Structures in Language
Specifically we develop a modified-ADIOS algorithm based on ADIOS Solan et al. (2005) to learn grammar structures, and use these grammar structures to learn the rules for identifying the semantic roles based on the context in which the grammar structures appeared.
Predicting the top and bottom ranks of billboard songs using Machine Learning
The music industry is a $130 billion industry.
