Is this you? Claim this profile to correct it, add a bio and choose the work people see first.
Claim this profileWorks173 from public data
- GraRep: Learning Graph Representations with Global Structural Information1,749
A novel model for learning vertex representations of weighted graphs that integrates global structural information of the graph into the learning process and significantly outperforms other state-of-the-art methods in such tasks.
- Deep Neural Networks for Learning Graph Representations1,063
A novel model for learning graph representations, which generates a low-dimensional vector representation for each vertex by capturing the graph structural information directly, and which outperforms other stat-of-the-art models in such tasks.
- Knowing What, How and Why: A Near Complete Solution for Aspect-Based Sentiment Analysis531
This paper introduces a new subtask under ABSA, named aspect sentiment triplet extraction (ASTE), and proposes a two-stage framework to address this task, which outperforms a few strong baselines adapted from state-of-the-art related methods.
- Attention Guided Graph Convolutional Networks for Relation Extraction496
Attention Guided Graph Convolutional Networks is proposed, a novel model which directly takes full dependency trees as inputs and can be understood as a soft-pruning approach that automatically learns how to selectively attend to the relevant sub-structures useful for the relation extraction task.
- Learning Latent Forests for Medical Relation Extraction401
This paper presents a novel method that automatically learns a retrieval model alternately with the programmer from weak supervision alternately with the programmer from weak supervision, i.e., the system’s performance with respect to the produced answers.
- Large language models in health care: Development, applications, and challenges365
It is believed that LLMs may be used widely in preconsultation, diagnosis, and management, with appropriate development and supervision, and hold tremendous promise in assisting with medical education, medical writing and other related applications.
- Position-Aware Tagging for Aspect Sentiment Triplet Extraction339
This work proposes the first end-to-end model with a novel position-aware tagging scheme that is capable of jointly extracting the triplets and results show that jointly capturing elements in the triplet using this approach leads to improved performance over the existing approaches.
- Reasoning with Latent Structure Refinement for Document-Level Relation Extraction332
This work proposes a novel model that empowers the relational reasoning across sentences by automatically inducing the latent document-level graph and develops a refinement strategy, which enables the model to incrementally aggregate relevant information for multi-hop reasoning.
- Two are Better than One: Joint Entity and Relation Extraction with Table-Sequence Encoders256
It is argued that it can be beneficial to design two distinct encoders to capture such two different types of information in the learning process, and proposed is the novel {\em table-sequence encoder} where two different encoder -- a table encoder and a sequence encoder are designed to help each other in the representation learning process.
- Joint Mention Extraction and Classification with Mention Hypergraphs241
A novel model is presented for the task of joint mention extraction and classification that is able to effectively capture overlapping mentions with unbounded lengths and can be extended to additionally capture mention heads explicitly in a joint manner under the same time complexity.
- Aligning Cross-Lingual Entities with Multi-Aspect Information189
This work investigates embedding-based approaches to encode entities from multilingual KGs into the same vector space, where equivalent entities are close to each other, and applies graph convolutional networks to combine multi-aspect information of entities to learn entity embeddings.
- Interventional Video Grounding with Dual Contrastive Learning175
A novel paradigm from the perspective of the causal inference, i.e., interventional video grounding (IVG) that leverages backdoor adjustment to deconfound the selection bias based on structured causal model (SCM) and do-calculus P(Y |do(X)).
- Neural Segmental Hypergraphs for Overlapping Mention Recognition171
This work proposes a novel segmental hypergraph representation to model overlapping entity mentions that are prevalent in many practical datasets and shows that the model built on top of such a new representation is able to capture features and interactions that cannot be captured by previous models while maintaining a low time complexity for inference.
- A generative model for parsing natural language to meaning representations158
The generative model is applied to the task of mapping sentences to hierarchical representations of their underlying meaning and achieves state-of-the-art performance when tested on two publicly available corpora.
- Densely Connected Graph Convolutional Networks for Graph-to-Sequence Learning149
This work introduces a dense connection strategy, proposing a novel Densely Connected Graph Convolutional Network (DCGCN), able to integrate both local and non-local features to learn a better structural representation of a graph.
- UER: An Open-Source Toolkit for Pre-training Models148
This work proposes an assemble-on-demand pre-training toolkit, namely Universal Encoder Representations (UER), which is loosely coupled, and encapsulated with rich modules, and built a model zoo, which contains pre-trained models based on different corpora, encoders, and targets.
- Better Punctuation Prediction with Dynamic Conditional Random Fields144
Empirical results show that the proposed approach is designed to jointly perform both sentence boundary and sentence type prediction, and punctuation prediction on speech utterances, outperforms an approach based on linear-chain conditional random fields and other previous approaches.
- cw2vec: Learning Chinese Word Embeddings with Stroke n-gram Information132
Empirical results show that the proposed cw2vec approach consistently outperforms state-of-the-art approaches such as word-based word2vec and GloVe, character-based CWE, component-based JWE and pixel-based GWE.
- Re-examining the Role of Schema Linking in Text-to-SQL130
This work provides a schema linking corpus based on the Spider text-to-SQL dataset, and builds a simple BERT-based baseline to perform a data-driven study on schema linking, finding when schema linking is done well, SLSQL demonstrates good performance on Spider despite its structural simplicity.
- A Neural Multi-digraph Model for Chinese NER with Gazetteers129
This work proposes a novel approach based on graph neural networks with a multi-digraph structure that captures the information that the gazetteers offer that is effective in incorporating rich gazetteser information while resolving ambiguities, outperforming previous approaches.
- 122
- Dependency-Guided LSTM-CRF for Named Entity Recognition112
This work proposes a simple yet effective dependency-guided LSTM-CRF model to encode the complete dependency trees and capture the above properties for the task of named entity recognition (NER).
- Neural Adaptation Layers for Cross-domain Named Entity Recognition112
This paper proposes lightweight yet effective methods for performing domain adaptation for neural models, which introduce adaptation layers on top of existing neural architectures, where no re-training using the source domain data is required.
- Learning Latent Opinions for Aspect-level Sentiment Classification112
A segmentation attention based LSTM model which can effectively capture the structural dependencies between the target and the sentiment expressions with a linear-chain conditional random field (CRF) layer is proposed.
- A Neural Transition-based Model for Nested Mention Recognition107
This paper introduces a scalable transition-based method to model the nested structure of mentions that gets the state-of-the-art performances in ACE datasets, showing its effectiveness in detecting nested mentions.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-12. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
Report an error
Wrong papers, two people merged into one, or a profile that should not be here? Tell us and we will fix or hide it.