Huiwei Zhou
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View article: Learning temporal difference embeddings for biomedical hypothesis generation
Learning temporal difference embeddings for biomedical hypothesis generation Open
Motivation Hypothesis generation (HG) refers to the discovery of meaningful implicit connections between disjoint scientific terms, which is of great significance for drug discovery, prediction of drug side effects and precision treatment.…
View article: Improving Biomedical Named Entity Recognition with Label Re-correction and Knowledge Distillation
Improving Biomedical Named Entity Recognition with Label Re-correction and Knowledge Distillation Open
Background: Biomedical named entities recognition is one of the most essential tasks in biomedical information extraction. Previous studies suffer from inadequate annotation datasets, especially the limited knowledge contained in them. Met…
View article: Global Context-enhanced Graph Convolutional Networks for Document-level Relation Extraction
Global Context-enhanced Graph Convolutional Networks for Document-level Relation Extraction Open
Document-level Relation Extraction (RE) is particularly challenging due to complex semantic interactions among multiple entities in a document. Among exiting approaches, Graph Convolutional Networks (GCN) is one of the most effective appro…
View article: DUT-NLP at MEDIQA 2019: An Adversarial Multi-Task Network to Jointly Model Recognizing Question Entailment and Question Answering
DUT-NLP at MEDIQA 2019: An Adversarial Multi-Task Network to Jointly Model Recognizing Question Entailment and Question Answering Open
In this paper, we propose a novel model called Adversarial Multi-Task Network (AMTN) for jointly modeling Recognizing Question Entailment (RQE) and medical Question Answering (QA) tasks. AMTN utilizes a pre-trained BioBERT model and an Int…
View article: DUT-BIM at MEDIQA 2019: Utilizing Transformer Network and Medical Domain-Specific Contextualized Representations for Question Answering
DUT-BIM at MEDIQA 2019: Utilizing Transformer Network and Medical Domain-Specific Contextualized Representations for Question Answering Open
In medical domain, given a medical question, it is difficult to manually select the most relevant information from a large number of search results. BioNLP 2019 proposes Question Answering (QA) task, which encourages the use of text mining…
View article: Combining Context and Knowledge Representations for Chemical-Disease Relation Extraction
Combining Context and Knowledge Representations for Chemical-Disease Relation Extraction Open
Automatically extracting the relationships between chemicals and diseases is significantly important to various areas of biomedical research and health care. Biomedical experts have built many large-scale knowledge bases (KBs) to advance t…
View article: Leveraging prior knowledge for protein–protein interaction extraction with memory network
Leveraging prior knowledge for protein–protein interaction extraction with memory network Open
Automatically extracting protein-protein interactions (PPIs) from biomedical literature provides additional support for precision medicine efforts. This paper proposes a novel memory network-based model (MNM) for PPI extraction, which leve…
View article: Exploiting syntactic and semantics information for chemical–disease relation extraction
Exploiting syntactic and semantics information for chemical–disease relation extraction Open
Identifying chemical-disease relations (CDR) from biomedical literature could improve chemical safety and toxicity studies. This article proposes a novel syntactic and semantic information exploitation method for CDR extraction. The propos…
View article: Hedge Scope Detection in Biomedical Texts: An Effective Dependency-Based Method
Hedge Scope Detection in Biomedical Texts: An Effective Dependency-Based Method Open
Hedge detection is used to distinguish uncertain information from facts, which is of essential importance in biomedical information extraction. The task of hedge detection is often divided into two subtasks: detecting uncertain cues and th…
View article: Learning Bilingual Sentiment Word Embeddings for Cross-language Sentiment Classification
Learning Bilingual Sentiment Word Embeddings for Cross-language Sentiment Classification Open
HuiWei Zhou, Long Chen, Fulin Shi, Degen Huang. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2015.