Subjects: Library Science，Information Science >> Library Science submitted time 2023-07-26 Cooperative journals: 《图书情报工作》
Abstract： [Purpose/significance] Based on the demands of Tang poetry knowledge service under the current big data environment, the knowledge graph of Tang poetry is constructed and intelligent knowledge service is provided on the basis of large-scale data of Tang poetry, which promotes the innovation of knowledge management and knowledge service mode of Tang poetry under the artificial intelligence environment.[Method/process] Based on the investigation of domain knowledge service requirements, this paper designs the Tang poetry ontology model driven by domain knowledge service,then uses knowledge extraction, knowledge fusion, knowledge reasoning and other technologies to automatically construct the knowledge graph of Tang poetry which unifies the representation and organization of Tang poetry domain data and achieve semantic processing of large-scale Tang poetry data.[Result/conclusion] This paper designs an intelligent knowledge service platform KnowPoetry based on the Tang poetry knowledge graph, which provides intelligent knowledge services such as knowledge exploration, spatio-temporal trajectory, semantic query in the field of Tang poetry, and promotes the innovative transformation of Tang poetry digital humanities research methods in artificial intelligence environment.
Subjects: Library Science，Information Science >> Information Science submitted time 2023-04-01 Cooperative journals: 《图书情报工作》
Abstract： [Purpose/significance] In order to further improve the effect of event extraction in the financial field, the correlation between the two subtasks of event extraction needs to be enhanced.[Method/process] This paper carried out related research about event extraction on Chinese financial texts,and proposed a joint extraction method of financial events that integrated the pre-training model and a multi-layer convolutional neural network. First, the pre-training model BERT captured the comprehensive semantic information of the sentence sequence, then accessed the multi-layer convolutional architecture designed in this paper——MultiCNN, hierarchically extracted local window and high-dimensional spatial semantic information, realized the two tasks of event recognition and element extraction at the same time, and then introduced contrast loss to further strengthen the association between the two tasks.[Result/conclusion] F1 has reached 82.20% on the Chinese financial event data set, which has a certain improvement over the benchmark extraction models.
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