Search Results for author: Xiang Wan

Found 12 papers, 6 papers with code

Cross-modal Memory Networks for Radiology Report Generation

1 code implementation ACL 2021 Zhihong Chen, Yaling Shen, Yan Song, Xiang Wan

Medical imaging plays a significant role in clinical practice of medical diagnosis, where the text reports of the images are essential in understanding them and facilitating later treatments.

Medical Diagnosis Text Generation

Dependency-driven Relation Extraction with Attentive Graph Convolutional Networks

1 code implementation ACL 2021 Yuanhe Tian, Guimin Chen, Yan Song, Xiang Wan

Syntactic information, especially dependency trees, has been widely used by existing studies to improve relation extraction with better semantic guidance for analyzing the context information associated with the given entities.

Relation Classification

Field Embedding: A Unified Grain-Based Framework for Word Representation

no code implementations NAACL 2021 Junjie Luo, Xi Chen, Jichao Sun, Yuejia Xiang, Ningyu Zhang, Xiang Wan

Word representations empowered with additional linguistic information have been widely studied and proved to outperform traditional embeddings.

Word Embeddings

Multi-Modal Active Learning for Automatic Liver Fibrosis Diagnosis based on Ultrasound Shear Wave Elastography

no code implementations2 Nov 2020 Lufei Gao, Ruisong Zhou, Changfeng Dong, Cheng Feng, Zhen Li, Xiang Wan, Li Liu

With the development of radiomics, noninvasive diagnosis like ultrasound (US) imaging plays a very important role in automatic liver fibrosis diagnosis (ALFD).

Active Learning

Generating Radiology Reports via Memory-driven Transformer

1 code implementation EMNLP 2020 Zhihong Chen, Yan Song, Tsung-Hui Chang, Xiang Wan

Particularly, this is the first work reporting the generation results on MIMIC-CXR to the best of our knowledge.

Text Generation

Named Entity Recognition for Social Media Texts with Semantic Augmentation

1 code implementation EMNLP 2020 Yuyang Nie, Yuanhe Tian, Xiang Wan, Yan Song, Bo Dai

In particular, we obtain the augmented semantic information from a large-scale corpus, and propose an attentive semantic augmentation module and a gate module to encode and aggregate such information, respectively.

Chinese Named Entity Recognition +2

Improving Named Entity Recognition with Attentive Ensemble of Syntactic Information

1 code implementation Findings of the Association for Computational Linguistics 2020 Yuyang Nie, Yuanhe Tian, Yan Song, Xiang Ao, Xiang Wan

Named entity recognition (NER) is highly sensitive to sentential syntactic and semantic properties where entities may be extracted according to how they are used and placed in the running text.

Chinese Named Entity Recognition NER

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