no code implementations • SMM4H (COLING) 2020 • Xiaoyu Zhao, Ying Xiong, Buzhou Tang
This is the system description of the Harbin Institute of Technology Shenzhen (HITSZ) team for the first and second subtasks of the fifth Social Media Mining for Health Applications (SMM4H) shared task in 2020.
no code implementations • 7 Mar 2024 • Hui Zong, Rongrong Wu, Jiaxue Cha, Erman Wu, Jiakun Li, Liang Tao, Zuofeng Li, Buzhou Tang, Bairong Shen
In this article, we review the recent advances in community challenges specific to Chinese biomedical text mining.
no code implementations • 20 Jan 2024 • Xianbing Zhao, Soujanya Poria, Xuejiao Li, Yixin Chen, Buzhou Tang
Recently, CLIP-based multimodal foundational models have demonstrated impressive performance on numerous multimodal tasks by learning the aligned cross-modal semantics of image and text pairs, but the multimodal foundational models are also unable to directly address scenarios involving modality absence.
no code implementations • 4 Jan 2024 • Shangyu Wu, Ying Xiong, Yufei Cui, Xue Liu, Buzhou Tang, Tei-Wei Kuo, Chun Jason Xue
Retrieval-based augmentations that aim to incorporate knowledge from an external database into language models have achieved great success in various knowledge-intensive (KI) tasks, such as question-answering and text generation.
Natural Language Understanding Neural Architecture Search +5
1 code implementation • 29 Dec 2023 • Wei Zhu, Xiaoling Wang, Mosha Chen, Buzhou Tang
Many teams from both the industry and academia participated in the shared tasks, and the top teams achieved amazing test results.
1 code implementation • 22 Oct 2023 • Wei Zhu, Xiaoling Wang, Huanran Zheng, Mosha Chen, Buzhou Tang
Biomedical language understanding benchmarks are the driving forces for artificial intelligence applications with large language model (LLM) back-ends.
1 code implementation • 9 Sep 2023 • Sicen Liu, Xiaolong Wang, Jingcheng Du, Yongshuai Hou, Xianbing Zhao, Hui Xu, Hui Wang, Yang Xiang, Buzhou Tang
Effectively medication recommendation with complex multimorbidity conditions is a critical task in healthcare.
1 code implementation • 1 Jun 2023 • Yuxin He, Jingyue Hu, Buzhou Tang
Under this framework, we experiment with 3 different training-inference schemes on 4 datasets (ACE05, RAMS, WikiEvents and MLEE) and discover that via training the model to extract all events in parallel, it can better distinguish the semantic boundary of each event and its ability to extract single event gets substantially improved.
1 code implementation • Empirical Methods in Natural Language Processing 2022 • Yuxin He, Buzhou Tang
Distinguished from the set-prediction NER framework, our method treats each entity as a sequence and is capable of recognizing discontinuous mentions.
no code implementations • 29 Apr 2022 • Sicen Liu, Xiaolong Wang, Yang Xiang, Hui Xu, Hui Wang, Buzhou Tang
It is a time-aware, event-aware and task-adaptive method with the following advantages: 1) modeling heterogeneous information and temporal information in a unified way and considering temporal irregular characteristics locally and globally respectively, 2) taking full advantage of correlations among different types of events via cross-event attention.
1 code implementation • 25 Jan 2022 • Sicen Liu, Xiaolong Wang, Yongshuai Hou, Ge Li, Hui Wang, Hui Xu, Yang Xiang, Buzhou Tang
As two important textual modalities in electronic health records (EHR), both structured data (clinical codes) and unstructured data (clinical narratives) have recently been increasingly applied to the healthcare domain.
no code implementations • 30 Jul 2021 • Haokui Zhang, Buzhou Tang, Wenze Hu, Xiaoyu Wang
Specifically, based on transformer, we propose a new network structure to compress the feature into a low dimensional space, and an inhomogeneous neighborhood relationship preserving (INRP) loss that aims to maintain high search accuracy.
2 code implementations • ACL 2022 • Ningyu Zhang, Mosha Chen, Zhen Bi, Xiaozhuan Liang, Lei LI, Xin Shang, Kangping Yin, Chuanqi Tan, Jian Xu, Fei Huang, Luo Si, Yuan Ni, Guotong Xie, Zhifang Sui, Baobao Chang, Hui Zong, Zheng Yuan, Linfeng Li, Jun Yan, Hongying Zan, Kunli Zhang, Buzhou Tang, Qingcai Chen
Artificial Intelligence (AI), along with the recent progress in biomedical language understanding, is gradually changing medical practice.
Ranked #1 on Semantic Similarity on CHIP-STS
no code implementations • 1 Jan 2021 • Zhaobin Xu, Baotian Hu, Buzhou Tang
It has two major parts.
no code implementations • 7 Apr 2020 • Xin Liu, Qingcai Chen, Yan Liu, Joanna Siebert, Baotian Hu, Xiang-Ping Wu, Buzhou Tang
We propose a Capsule network-based method to Decompose the unsupervised word Embedding of an ambiguous word into context specific Sense embedding, called CapsDecE2S.
no code implementations • 9 Mar 2020 • Xianpei Han, Zhichun Wang, Jiangtao Zhang, Qinghua Wen, Wenqi Li, Buzhou Tang, Qi. Wang, Zhifan Feng, Yang Zhang, Yajuan Lu, Haitao Wang, Wenliang Chen, Hao Shao, Yubo Chen, Kang Liu, Jun Zhao, Taifeng Wang, Kezun Zhang, Meng Wang, Yinlin Jiang, Guilin Qi, Lei Zou, Sen Hu, Minhao Zhang, Yinnian Lin
Knowledge graph models world knowledge as concepts, entities, and the relationships between them, which has been widely used in many real-world tasks.
no code implementations • 1 Dec 2019 • Lisai Zhang, Qingcai Chen, Dongfang Li, Buzhou Tang
In the framework, the visual features are obtained through a visualization and fusion mechanism.
Natural Language Inference Natural Language Understanding +2
no code implementations • WS 2019 • Ying Xiong, Yedan Shen, Yuanhang Huang, Shuai Chen, Buzhou Tang, Xiaolong Wang, Qingcai Chen, Jun Yan, Yi Zhou
The Biological Text Mining Unit at BSC and CNIO organized the first shared task on chemical {\&} drug mention recognition from Spanish medical texts called PharmaCoNER (Pharmacological Substances, Compounds and proteins and Named Entity Recognition track) in 2019, which includes two tracks: one for NER offset and entity classification (track 1) and the other one for concept indexing (track 2).
no code implementations • WS 2019 • Dongfang Li, Ying Xiong, Baotian Hu, Hanyang Du, Buzhou Tang, Qingcai Chen
In this paper, we present our approaches for trigger word detection (task 1) and the identification of its thematic role (task 2) in AGAC track of BioNLP Open Shared Task 2019.
no code implementations • 2 Sep 2019 • Linfeng Li, Peng Wang, Yao Wang, Jinpeng Jiang, Buzhou Tang, Jun Yan, Sheng-Hui Wang, Yu-Ting Liu
This paper proposes an algorithm named as PrTransH to learn embedding vectors from real world EMR data based medical knowledge.
no code implementations • WS 2019 • Shuai Chen, Yuanhang Huang, Xiaowei Huang, Haoming Qin, Jun Yan, Buzhou Tang
This is the system description of the Harbin Institute of Technology Shenzhen (HITSZ) team for the first and second subtasks of the fourth Social Media Mining for Health Applications (SMM4H) shared task in 2019.
no code implementations • EMNLP 2018 • Jing Chen, Qingcai Chen, Xin Liu, Haijun Yang, Daohe Lu, Buzhou Tang
As the largest manually annotated public Chinese SSEI corpus in the bank domain, the BQ corpus is not only useful for Chinese question semantic matching research, but also a significant resource for cross-lingual and cross-domain SSEI research.
no code implementations • COLING 2018 • Xin Liu, Qingcai Chen, Chong Deng, Huajun Zeng, Jing Chen, Dongfang Li, Buzhou Tang
In this paper, we first use a search engine to collect large-scale question pairs related to high-frequency words from various domains, then filter irrelevant pairs by the Wasserstein distance, and finally recruit three annotators to manually check the left pairs.
no code implementations • EMNLP 2017 • Bofang Li, Tao Liu, Zhe Zhao, Buzhou Tang, Aleks Drozd, R, Anna Rogers, Xiaoyong Du
The number of word embedding models is growing every year.
1 code implementation • COLING 2016 • Yang Xiang, Xiaoqiang Zhou, Qingcai Chen, Zhihui Zheng, Buzhou Tang, Xiaolong Wang, Yang Qin
In community question answering (cQA), the quality of answers are determined by the matching degree between question-answer pairs and the correlation among the answers.
no code implementations • IJCNLP 2015 • Xiaoqiang Zhou, Baotian Hu, Qingcai Chen, Buzhou Tang, Xiaolong Wang
In this paper, the answer selection problem in community question answering (CQA) is regarded as an answer sequence labeling task, and a novel approach is proposed based on the recurrent architecture for this problem.