Knowledge Commons of Institute of Automation,CAS
Species Classification for Neuroscience Literature Based on Span of Interest Using Sequence-to-Sequence Learning Model | |
Zhu, Hongyin1![]() ![]() ![]() ![]() | |
发表期刊 | https://www.frontiersin.org/articles/10.3389/fnhum.2020.00128/full
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2020-04 | |
卷号 | 14期号:1页码:128 |
摘要 | Large-scale neuroscience literature call for effective methods to mine the knowledge from species perspective to link the brain and neuroscience communities, neurorobotics, computing devices, and AI research communities. Structured knowledge can motivate researchers to better understand the functionality and structure of the brain and link the related resources and components. However, the abstracts of massive scientific works do not explicitly mention the species. Therefore, in addition to dictionary-based methods, we need to mine species using cognitive computing models that are more like the human reading process, and these methods can take advantage of the rich information in the literature. We also enable the model to automatically distinguish whether the mentioned species is the main research subject. Distinguishing the two situations can generate value at different levels of knowledge management. We propose SpecExplorer project which is used to explore the knowledge associations of different species for brain and neuroscience. This project frees humans from the tedious task of classifying neuroscience literature by species. Species classification task belongs to the multi-label classification which is more complex than the single-label classification due to the correlation between labels. To resolve this problem, we present the sequence-to-sequence classification framework to adaptively assign multiple species to the literature. To model the structure information of documents, we propose the hierarchical attentive decoding (HAD) to extract span of interest (SOI) for predicting each species. We create three datasets from PubMed and PMC corpora. We present two versions of annotation criteria (mention-based annotation and semantic-based annotation) for species research. Experiments demonstrate that our approach achieves improvements in the final results. Finally, we perform species-based analysis of brain diseases, brain cognitive functions, and proteins related to the hippocampus and provide potential research directions for certain species. |
关键词 | brain science neuroscience PubMed |
收录类别 | SCI |
语种 | 英语 |
WOS记录号 | WOS:000531605700001 |
七大方向——子方向分类 | 文字识别与文档分析 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/39288 |
专题 | 脑图谱与类脑智能实验室_类脑认知计算 |
通讯作者 | Zeng, Yi |
作者单位 | 1.Institute of Automation, Chinese Academy of Sciences 2.Department of Computer Science, University of Copenhagen |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Zhu, Hongyin,Zeng, Yi,Wang, Dongsheng,et al. Species Classification for Neuroscience Literature Based on Span of Interest Using Sequence-to-Sequence Learning Model[J]. https://www.frontiersin.org/articles/10.3389/fnhum.2020.00128/full,2020,14(1):128. |
APA | Zhu, Hongyin,Zeng, Yi,Wang, Dongsheng,&Huangfu, Cunqing.(2020).Species Classification for Neuroscience Literature Based on Span of Interest Using Sequence-to-Sequence Learning Model.https://www.frontiersin.org/articles/10.3389/fnhum.2020.00128/full,14(1),128. |
MLA | Zhu, Hongyin,et al."Species Classification for Neuroscience Literature Based on Span of Interest Using Sequence-to-Sequence Learning Model".https://www.frontiersin.org/articles/10.3389/fnhum.2020.00128/full 14.1(2020):128. |
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