Is this you? Claim this profile to correct it, add a bio and choose the work people see first.
Claim this profileWorks161 from public data
- Unified Named Entity Recognition as Word-Word Relation Classification392
This work develops a neural framework, in which the unified NER is modeled as a 2D grid of word pairs, and proposes multi-granularity 2D convolutions for better refining the grid representations.
- A neural joint model for entity and relation extraction from biomedical text251
It is demonstrated that the model based on neural networks is effective for biomedical entity and relation extraction and parameter sharing is an alternative method for neural models to jointly process this task.
- ICD Coding from Clinical Text Using Multi-Filter Residual Convolutional Neural Network226
A Multi-Filter Residual Convolutional Neural Network (MultiResCNN) for ICD coding that utilizes a multi-filter convolutional layer to capture various text patterns with different lengths and a residual convolutionAL layer to enlarge the receptive field.
- Fine-Tuning Bidirectional Encoder Representations From Transformers (BERT)–Based Models on Large-Scale Electronic Health Record Notes: An Empirical Study178
This work shows that BERT-based models have achieved state-of-the-art performance for biomedical and clinical entity normalization and can be readily fine-tuned to normalize any kind of named entities.
- MRN: A Locally and Globally Mention-Based Reasoning Network for Document-Level Relation Extraction125
A novel mention-based reasoning (MRN) module based on explicitly and collaboratively local and global reasoning is proposed and a co-predictor module to predict entity relations based on local and global entity and relation representations jointly is designed.
- A Span-Based Model for Joint Overlapped and Discontinuous Named Entity Recognition123
Experimental results on multiple benchmark datasets show that the proposed span-based model can be regarded as a relation extraction paradigm essentially and is highly competitive for overlapped and discontinuous NER.
- LasUIE: Unifying Information Extraction with Latent Adaptive Structure-aware Generative Language Model114
In this work, a novel structure-aware GLM is proposed, fully unleashing the power of syntactic knowledge for UIE, and rich task-adaptive structural bias is learned that greatly resolves the UIE crux, the long-range dependence issue and boundary identifying.
- 110
- HiTrans: A Transformer-Based Context- and Speaker-Sensitive Model for Emotion Detection in Conversations109
A transformer-based context- and speaker-sensitive model for EDC, namely HiTrans, which consists of two hierarchical transformers, which outperforms previous state-of-the-art models.
- Encoder-Decoder Based Unified Semantic Role Labeling with Label-Aware Syntax89
A novel unified SRL framework based on the sequence-to-sequence architecture with double enhancement in both the encoder and decoder sides is investigated, and in-depth analysis demonstrates that the model can effectively capture the correlations between syntactic and SRL structures.
- Rethinking Boundaries: End-To-End Recognition of Discontinuous Mentions with Pointer Networks84
An innovative model for discontinuous NER based on pointer networks, where the pointer simultaneously decides whether a token at each decoding frame constitutes an entity mention and where the next constituent token is, which is the kernel source to NER.
- 78
- Extraction of Information Related to Adverse Drug Events from Electronic Health Record Notes: Design of an End-to-End Model Based on Deep Learning78
Deep learning models can significantly improve the performance of ADE-related information extraction and may be effective for named entity recognition and relation extraction, but it depends on the methods, data, and other factors.
- Better Combine Them Together! Integrating Syntactic Constituency and Dependency Representations for Semantic Role Labeling76
A TreeLSTM-based integration, collaboratively learning the phrasal boundaries from the constituency and the semantic relations from dependency, and a label-aware GCN solution for simultaneously modeling the syntactic edges and labels are considered.
- Inheriting the Wisdom of Predecessors: A Multiplex Cascade Framework for Unified Aspect-based Sentiment Analysis66
This work presents a novel multiplex cascade framework for unified ABSA and enhances the ABSA performances on 28 subtasks (7×4 datasets) with big margins by integrating POS tag and syntactic dependency information for term boundary and pairing identification.
- A transition-based joint model for disease named entity recognition and normalization65
A transition‐based model to jointly perform disease named entity recognition and normalization is proposed, casting the output construction process into an incremental state transition process, learning sequences of transition actions globally, which correspond to joint structural outputs.
- Emoji-Based Sentiment Analysis Using Attention Networks64
This article investigates a sentiment analysis model based on bidirectional long short-term memory, and the model has two advantages compared with the existing work: it does not need feature engineering and utilizes the attention approach to model the impact of emojis on text.
- Joint models for extracting adverse drug events from biomedical text61
Experimental results on a standard ADE corpus show that the discrete joint model outperforms a state-of-the-art baseline pipeline significantly and when discrete features are replaced by neural features, the recall is further improved.
- 51
- 49
- 49
- Recognizing irregular entities in biomedical text via deep neural networks49
A neural network model called Bi-LSTM-CRF that consists of bidirectional (Bi) long short-term memories (LSTMs) and conditional random fields (CRFs) to identify regular entities and the components of irregular entities and it is shown that the method of automatically combining the components is as effective as the methods of manually designing rules.
- 44
- Mastering the Explicit Opinion-Role Interaction: Syntax-Aided Neural Transition System for Unified Opinion Role Labeling43
This work investigates a novel solution by revisiting the transition architecture, and augmenting it with a pointer network (PointNet), and proposes a unified dependency-opinion graph (UDOG), co-modeling the syntactic dependency structure and the partial opinion-role structure.
- Fine-grained depression analysis based on Chinese micro-blog reviews43
A neural model for joint depression degree and cause prediction is designed and compared with several widely-used neural models such as TextCNN, BiLSTM and BERT, which shows that neural models achieve promising results, but there is still room for improvement.
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.