Search Results for author:
Found 19 papers, 4 papers with code
Taxonomy and Analysis of Sensitive User Queries in Generative AI Search
Although there has been a growing interest among industries to integrate generative LLMs into their services, limited experiences and scarcity of resources acts as a barrier in launching and servicing large-scale LLM-based conversational services.
Improving out-of-distribution generalization in graphs via hierarchical semantic environments
Out-of-distribution (OOD) generalization in the graph domain is challenging due to complex distribution shifts and a lack of environmental contexts.
Clinical Note Owns its Hierarchy: Multi-Level Hypergraph Neural Networks for Patient-Level Representation Learning
Directly adopting existing hypergraph methods on clinical notes cannot sufficiently utilize the hierarchy information of the patient, which can degrade clinical semantic information by (1) frequent neutral words and (2) hierarchies with imbalanced distribution.
SPGP: Structure Prototype Guided Graph Pooling
In this paper, we propose Structure Prototype Guided Pooling (SPGP) that utilizes prior graph structures to overcome the limitation.
Triangular Contrastive Learning on Molecular Graphs
Recent contrastive learning methods have shown to be effective in various tasks, learning generalizable representations invariant to data augmentation thereby leading to state of the art performances.
Sparse Structure Learning via Graph Neural Networks for Inductive Document Classification
To address these challenges, we propose a novel GNN-based sparse structure learning model for inductive document classification.
Handling Long-Tail Queries with Slice-Aware Conversational Systems
We have been witnessing the usefulness of conversational AI systems such as Siri and Alexa, directly impacting our daily lives.
TeamTat: a collaborative text annotation tool
Manually annotated data is key to developing text-mining and information-extraction algorithms.
BioConceptVec: creating and evaluating literature-based biomedical concept embeddings on a large scale
Capturing the semantics of related biological concepts, such as genes and mutations, is of significant importance to many research tasks in computational biology such as protein-protein interaction detection, gene-drug association prediction, and biomedical literature-based discovery.
Deep learning with sentence embeddings pre-trained on biomedical corpora improves the performance of finding similar sentences in electronic medical records
For the post challenge, the performance of both Random Forest and the Encoder Network was improved; in particular, the correlation of the Encoder Network was improved by ~13%.
A Fast Deep Learning Model for Textual Relevance in Biomedical Information Retrieval
Publications in the life sciences are characterized by a large technical vocabulary, with many lexical and semantic variations for expressing the same concept.
Hybrid Approach of Relation Network and Localized Graph Convolutional Filtering for Breast Cancer Subtype Classification
In this paper, we proposed a hybrid model, which integrates two key components 1) graph convolution neural network (graph CNN) and 2) relation network (RN).
Deep Learning for Biomedical Information Retrieval: Learning Textual Relevance from Click Logs
We describe a Deep Learning approach to modeling the relevance of a document{'}s text to a query, applied to biomedical literature.
BioCreative VI Precision Medicine Track: creating a training corpus for mining protein-protein interactions affected by mutations
The Precision Medicine Track in BioCre-ative VI aims to bring together the Bi-oNLP community for a novel challenge focused on mining the biomedical litera-ture in search of mutations and protein-protein interactions (PPI).
Bridging the Gap: Incorporating a Semantic Similarity Measure for Effectively Mapping PubMed Queries to Documents
Here we present a query-document similarity measure motivated by the Word Mover's Distance.
Extracting drug–drug interactions from literature using a rich feature-based linear kernel approach
In this work, we propose an efficient and scalable system using a linear kernel to identify DDI information.
