Lishuang Li

Also published as: LiShuang Li


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Document-level Biomedical Relation Extraction Based on Multi-Dimensional Fusion Information and Multi-Granularity Logical Reasoning
Lishuang Li | Ruiyuan Lian | Hongbin Lu | Jingyao Tang
Proceedings of the 29th International Conference on Computational Linguistics

Document-level biomedical relation extraction (Bio-DocuRE) is an important branch of biomedical text mining that aims to automatically extract all relation facts from the biomedical text. Since there are a considerable number of relations in biomedical documents that need to be judged by other existing relations, logical reasoning has become a research hotspot in the past two years. However, current models with reasoning are single-granularity only based on one element information, ignoring the complementary fact of different granularity reasoning information. In addition, obtaining rich document information is a prerequisite for logical reasoning, but most of the previous models cannot sufficiently utilize document information, which limits the reasoning ability of the model. In this paper, we propose a novel Bio-DocuRE model called FILR, based on Multi-Dimensional Fusion Information and Multi-Granularity Logical Reasoning. Specifically, FILR presents a multi-dimensional information fusion module MDIF to extract sufficient global document information. Then FILR proposes a multi-granularity reasoning module MGLR to obtain rich inference information through the reasoning of both entity-pairs and mention-pairs. We evaluate our FILR model on two widely used biomedical corpora CDR and GDA. Experimental results show that FILR achieves state-of-the-art performance.

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基于中文电子病历知识图谱的实体对齐研究(Research on Entity Alignment Based on Knowledge Graph of Chinese Electronic Medical Record)
Lishuang Li (李丽双) | Jiangyuan Dong (董姜媛)
Proceedings of the 21st Chinese National Conference on Computational Linguistics


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基于平行交互注意力网络的中文电子病历实体及关系联合抽取(Parallel Interactive Attention Network for Joint Entity and Relation Extraction Based on Chinese Electronic Medical Record)
LiShuang Li (李丽双) | Zehao Wang (王泽昊) | Xueyang Qin (秦雪洋) | Yuan Guanghui (袁光辉)
Proceedings of the 21st Chinese National Conference on Computational Linguistics



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Hierarchical Attention Based Position-Aware Network for Aspect-Level Sentiment Analysis
Lishuang Li | Yang Liu | AnQiao Zhou
Proceedings of the 22nd Conference on Computational Natural Language Learning

Aspect-level sentiment analysis aims to identify the sentiment of a specific target in its context. Previous works have proved that the interactions between aspects and the contexts are important. On this basis, we also propose a succinct hierarchical attention based mechanism to fuse the information of targets and the contextual words. In addition, most existing methods ignore the position information of the aspect when encoding the sentence. In this paper, we argue that the position-aware representations are beneficial to this task. Therefore, we propose a hierarchical attention based position-aware network (HAPN), which introduces position embeddings to learn the position-aware representations of sentences and further generate the target-specific representations of contextual words. The experimental results on SemEval 2014 dataset show that our approach outperforms the state-of-the-art methods.


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Extracting Biomedical Event Using Feature Selection and Word Representation
Xinyu He | Lishuang Li | Jieqiong Zheng | Meiyue Qin
Proceedings of the 4th BioNLP Shared Task Workshop


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Improving Feature-Based Biomedical Event Extraction System by Integrating Argument Information
Lishuang Li | Yiwen Wang | Degen Huang
Proceedings of the BioNLP Shared Task 2013 Workshop


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Mining Large-scale Comparable Corpora from Chinese-English News Collections
Degen Huang | Lian Zhao | Lishuang Li | Haitao Yu
Coling 2010: Posters


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HMM and CRF Based Hybrid Model for Chinese Lexical Analysis
Degen Huang | Xiao Sun | Shidou Jiao | Lishuang Li | Zhuoye Ding | Ru Wan
Proceedings of the Sixth SIGHAN Workshop on Chinese Language Processing


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Hybrid Models for Chinese Named Entity Recognition
Lishuang Li | Tingting Mao | Degen Huang | Yuansheng Yang
Proceedings of the Fifth SIGHAN Workshop on Chinese Language Processing