Search Results for author:
Found 33 papers, 15 papers with code
Self Question-answering: Aspect-based Sentiment Analysis by Role Flipped Machine Reading Comprehension
In this paper, we investigate the unified ABSA task from the perspective of Machine Reading Comprehension (MRC) by observing that the aspect and the opinion terms can serve as the query and answer in MRC interchangeably.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+3
ST-Align: A Multimodal Foundation Model for Image-Gene Alignment in Spatial Transcriptomics
Spatial transcriptomics (ST) provides high-resolution pathological images and whole-transcriptomic expression profiles at individual spots across whole-slide scales.
ShapeFormer: Shapelet Transformer for Multivariate Time Series Classification
In this paper, we propose a novel Shapelet Transformer (ShapeFormer), which comprises class-specific and generic transformer modules to capture both of these features.
EnzChemRED, a rich enzyme chemistry relation extraction dataset
EnzChemRED consists of 1, 210 expert curated PubMed abstracts in which enzymes and the chemical reactions they catalyze are annotated using identifiers from the UniProt Knowledgebase (UniProtKB) and the ontology of Chemical Entities of Biological Interest (ChEBI).
PubTator 3.0: an AI-powered Literature Resource for Unlocking Biomedical Knowledge
PubTator 3. 0 (https://www. ncbi. nlm. nih. gov/research/pubtator3/) is a biomedical literature resource using state-of-the-art AI techniques to offer semantic and relation searches for key concepts like proteins, genetic variants, diseases, and chemicals.
Taiyi: A Bilingual Fine-Tuned Large Language Model for Diverse Biomedical Tasks
The case study involving additional biomedical NLP tasks further shows Taiyi's considerable potential for bilingual biomedical multi-tasking.
3D VR Sketch Guided 3D Shape Prototyping and Exploration
3D shape modeling is labor-intensive, time-consuming, and requires years of expertise.
BioREx: Improving Biomedical Relation Extraction by Leveraging Heterogeneous Datasets
State-of-the-art methods were used primarily to train machine learning models on individual RE datasets, such as protein-protein interaction and chemical-induced disease relation.
Unsupervised Domain-agnostic Fake News Detection using Multi-modal Weak Signals
Also, we propose a novel technique to construct news datasets minimizing the latent biases in existing news datasets.
AIONER: All-in-one scheme-based biomedical named entity recognition using deep learning
Biomedical named entity recognition (BioNER) seeks to automatically recognize biomedical entities in natural language text, serving as a necessary foundation for downstream text mining tasks and applications such as information extraction and question answering.
Towards 3D VR-Sketch to 3D Shape Retrieval
In this paper, we offer a different perspective towards answering these questions -- we study the use of 3D sketches as an input modality and advocate a VR-scenario where retrieval is conducted.
Fine-Grained VR Sketching: Dataset and Insights
We then, for the first time, study the scenario of fine-grained 3D VR sketch to 3D shape retrieval, as a novel VR sketching application and a proving ground to drive out generic insights to inform future research.
Structure-Aware 3D VR Sketch to 3D Shape Retrieval
In particular, we propose to use a triplet loss with an adaptive margin value driven by a "fitting gap", which is the similarity of two shapes under structure-preserving deformations.
Learn Continuously, Act Discretely: Hybrid Action-Space Reinforcement Learning For Optimal Execution
On the one hand, the continuous action space using percentage changes in prices is preferred for generalization.
Assigning Species Information to Corresponding Genes by a Sequence Labeling Framework
The automatic assignment of species information to the corresponding genes in a research article is a critically important step in the gene normalization task, whereby a gene mention is normalized and linked to a database record or identifier by a text-mining algorithm.
BioRED: A Rich Biomedical Relation Extraction Dataset
However, most existing benchmarking datasets for bio-medical RE only focus on relations of a single type (e. g., protein-protein interactions) at the sentence level, greatly limiting the development of RE systems in biomedicine.
Ranked #1 on
Named Entity Recognition (NER)
on BioRED
PENS: A Dataset and Generic Framework for Personalized News Headline Generation
To build up a benchmark for this problem, we publicize a large-scale dataset named PENS (PErsonalized News headlineS).
Embracing Domain Differences in Fake News: Cross-domain Fake News Detection using Multi-modal Data
Hence, this work: (1) proposes a novel framework that jointly preserves domain-specific and cross-domain knowledge in news records to detect fake news from different domains; and (2) introduces an unsupervised technique to select a set of unlabelled informative news records for manual labelling, which can be ultimately used to train a fake news detection model that performs well for many domains while minimizing the labelling cost.
Meet Changes with Constancy: Learning Invariance in Multi-Source Translation
Multi-source neural machine translation aims to translate from parallel sources of information (e. g. languages, images, etc.)
Artificial Intelligence (AI) in Action: Addressing the COVID-19 Pandemic with Natural Language Processing (NLP)
The COVID-19 pandemic has had a significant impact on society, both because of the serious health effects of COVID-19 and because of public health measures implemented to slow its spread.
PhenoTagger: A Hybrid Method for Phenotype Concept Recognition using Human Phenotype Ontology
In this paper, we propose PhenoTagger, a hybrid method that combines both dictionary and machine learning-based methods to recognize Human Phenotype Ontology (HPO) concepts in unstructured biomedical text.
METEOR: Learning Memory and Time Efficient Representations from Multi-modal Data Streams
To address this problem, we present METEOR, a novel MEmory and Time Efficient Online Representation learning technique, which: (1) learns compact representations for multi-modal data by sharing parameters within semantically meaningful groups and preserves the domain-agnostic semantics; (2) can be accelerated using parallel processes to accommodate different stream rates while capturing the temporal changes of the units; and (3) can be easily extended to capture implicit/explicit external knowledge related to multi-modal data streams.
OMBA: User-Guided Product Representations for Online Market Basket Analysis
OMBA jointly learns representations for products and users such that they preserve the temporal dynamics of product-to-product and user-to-product associations.
GoChat: Goal-oriented Chatbots with Hierarchical Reinforcement Learning
A chatbot that converses like a human should be goal-oriented (i. e., be purposeful in conversation), which is beyond language generation.
EHANet: An Effective Hierarchical Aggregation Network for Face Parsing
In recent years, benefiting from deep convolutional neural networks (DCNNs), face parsing has developed rapidly.
Ranked #6 on
Face Parsing
on CelebAMask-HQ
Bayesian Nonparametric Space Partitions: A Survey
Bayesian nonparametric space partition (BNSP) models provide a variety of strategies for partitioning a $D$-dimensional space into a set of blocks.
Real-time Segmentation and Facial Skin Tones Grading
Modern approaches for semantic segmention usually pay too much attention to the accuracy of the model, and therefore it is strongly recommended to introduce cumbersome backbones, which brings heavy computation burden and memory footprint.
HybridNetSeg: A Compact Hybrid Network for Retinal Vessel Segmentation
A large number of retinal vessel analysis methods based on image segmentation have emerged in recent years.
Reading Like HER: Human Reading Inspired Extractive Summarization
In this work, we re-examine the problem of extractive text summarization for long documents.
Ranked #8 on
Extractive Text Summarization
on CNN / Daily Mail
USTAR: Online Multimodal Embedding for Modeling User-Guided Spatiotemporal Activity
Building spatiotemporal activity models for people's activities in urban spaces is important for understanding the ever-increasing complexity of urban dynamics.
Unsupervised Neural Aspect Extraction with Sememes
Aspect extraction relies on identifying aspects by discovering coherence among words, which is challenging when word meanings are diversified and processing on short texts.
Aspect Extraction
Aspect Term Extraction and Sentiment Classification
+1
An attention-based BiLSTM-CRF approach to document-level chemical named entity recognition
Motivation: In biomedical research, chemical is an important class of entities, and chemical named entity recognition (NER) is an important task in the field of biomedical information extraction.
Ranked #6 on
Named Entity Recognition (NER)
on BC4CHEMD
