融合依存信息Attention机制的药物关系抽取研究
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摘要
药物关系(Drug-Drug Interaction,DDI)抽取是生物医学关系抽取领域的重要分支,现有方法主要强调实体、位置等信息对关系抽取的影响。相关研究表明,依存信息对于关系抽取具有重要作用,如何合理利用依存信息是关系抽取研究中需要解决的问题。该文提出一种融合依存信息Attention机制的药物关系抽取模型,衡量最短依存路径与句子的相关性,捕捉对实体间关系有用的信息。首先使用双向GRU(BiGRU)网络分别学习原句子和最短依存路径(Shortest Dependency Path,SDP)的语义信息和上下文信息,然后通过Attention机制将SDP信息与原句子信息融合,最后利用融合依存信息之后的句子表示进行分类预测。在DDIExtraction2013语料上进行了实验评估,模型F值为73.72%。
Drug-Drug Interaction(DDI)extraction is an important issue in biomedical relationship extraction.Most of existing methods emphasize the key information such as entities and positions in the sentences.To further exploit the sentence structure,this paper proposes a Drug-Drug interaction extraction model based on the attention mechanism over the dependency.The correlation between the shortest dependency path and the sentence is measured to capture the useful information.Firstly,this model uses BiGRU network to learn the semantic information and context information of the original sentence and the Shortest Dependency Path(SDP)respectively.Secondly,the SDP information is incorporated into the original sentence information through the Attention mechanism.Finally,the final sentence representation is used to classify and predict DDI.This approach is evaluated on DDIExtraction 2013 corpus,yielding a micro F-scores of 73.72%.
Drug-Drug Interaction(DDI)extraction is an important issue in biomedical relationship extraction.Most of existing methods emphasize the key information such as entities and positions in the sentences.To further exploit the sentence structure,this paper proposes a Drug-Drug interaction extraction model based on the attention mechanism over the dependency.The correlation between the shortest dependency path and the sentence is measured to capture the useful information.Firstly,this model uses BiGRU network to learn the semantic information and context information of the original sentence and the Shortest Dependency Path(SDP)respectively.Secondly,the SDP information is incorporated into the original sentence information through the Attention mechanism.Finally,the final sentence representation is used to classify and predict DDI.This approach is evaluated on DDIExtraction 2013 corpus,yielding a micro F-scores of 73.72%.
引文
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[15]Herrerozazo M,Segurabedmar I,Declerck T.The DDI corpus:an annotated corpus with pharmacological substances and Drug-Drug interactions[J].Journal of Biomedical Informatics,2013(46):914-920.
[16]Segurabedmar I,Herrerozazo M.Lessons learnt from the DDIExtraction-2013Shared Task[J].Journal of Biomedical Informatics,2014(51):152-164.
[17]Yi Z,et al.Drug-Drug interaction extraction via recurrent neural network with multiple attention layers[C]//Proceedings of the arXiv:1705.03261.2017.
[18]Zheng W,et al.A graph kernel based on context vectors for extracting Drug-Drug interactions[J].Journal of Biomedical Informatics,2016(61):34-43.
[19]Raihani A,Laachfoubi N.Extracting Drug-Drug interactions from biomedical text using a feature-based kernel approach[J].Journal of Theoretical and Applied Information Technology,2016(92):109-120.
[20]Zhao Z,et al.Drug drug interaction extraction from biomedical literature using syntax convolutional neural network[J].Bioinformatics,2016,32(22):3444-3453.
[2]Segura Bedmar I,Martinez P,Herrero Zazo M.Semeval-2013task 9:Extraction of Drug-Drug interactions from biomedical texts(DDIextraction 2013)[J].Proceeding of Semeval,2013,2(SemEval):341-350.
[3]Kim S,et al.Extracting Drug-Drug interactions from literature using a rich feature-based linear kernel approach[J].Journal of Biomedical Informatics,2015(55):23-30.
[4]Liu S,et al.Drug-Drug interaction extraction via convolutional neural networks[J].Comput Math Methods Med,2016:6918381.
[5]Quan C,et al.Multichannel convolutional neural network for biological relation extraction[J].BioMed Research International,2016(2-1):1-10.
[6]Bahdanau D,Cho K,Bengio Y.Neural machine translation by jointly learning to align and translate[J].arXiv:1409.0473.2014.
[7]Asada M,Miwa M,Sasaki Y.Extracting Drug-Drug interactions with attention CNNs[C]//Proceedings of the BioNLP 2017 Workshop,Vancouver,Canada,2017:9-18.
[8]Sahu SK,Anand A.Drug-Drug interaction extraction from biomedical text using long short term memory network[J].arXiv:1701.08303.2017.
[9]Zhou D,Miao L,He Y.Position-aware deep multitask learning for Drug-Drug interaction extraction[J].Artificial Intelligence in Medicine,2018,87:1-8.
[10]Liu Y,et al.A dependency-based neural network for relation classification[J].arXiv:1507.04646.2015.
[11]Yan X,et al.Classifying relations via long short term memory networks along shortest dependency path[C]//Proceedings of EMULP,2015:1785-1794.
[12]Miwa M,Bansal M.End-to-end relation extraction using lstms on sequences and tree structures[J].arXiv:1601.00770.2016.
[13]Zhang Y,Zheng W,Lin H,et al.Drug-Drug interaction extraction via hierarchical RNNs on sequence and shortest dependency paths[J].Bioinformatics,2017(34):828-835.
[14]Kyunghyun Cho,et al.Learning phrase representations using RNN encoder-decoder for statistical machine translation[C]//Proceedings of the 2014Conference on Empirical Methods in Natural Language Processing(EMNLP),Doha,Qatar,2014:1724-1734.
[15]Herrerozazo M,Segurabedmar I,Declerck T.The DDI corpus:an annotated corpus with pharmacological substances and Drug-Drug interactions[J].Journal of Biomedical Informatics,2013(46):914-920.
[16]Segurabedmar I,Herrerozazo M.Lessons learnt from the DDIExtraction-2013Shared Task[J].Journal of Biomedical Informatics,2014(51):152-164.
[17]Yi Z,et al.Drug-Drug interaction extraction via recurrent neural network with multiple attention layers[C]//Proceedings of the arXiv:1705.03261.2017.
[18]Zheng W,et al.A graph kernel based on context vectors for extracting Drug-Drug interactions[J].Journal of Biomedical Informatics,2016(61):34-43.
[19]Raihani A,Laachfoubi N.Extracting Drug-Drug interactions from biomedical text using a feature-based kernel approach[J].Journal of Theoretical and Applied Information Technology,2016(92):109-120.
[20]Zhao Z,et al.Drug drug interaction extraction from biomedical literature using syntax convolutional neural network[J].Bioinformatics,2016,32(22):3444-3453.