Subjects: Library Science，Information Science >> Automation method and equipment in intelligence process submitted time 2024-03-12
Abstract： Purpose/Significance The data storytelling method of integrating interpretability results provides a new strategy to solve the problems of difficult data cognition, difficult to understand prediction results and low reliability of model decision-making. Method/Process This paper summarizes the interpretation form of model-agnostic local interpretability technology, the narrative structure of data stories and the methods used in the current research on data storytelling. Based on the interpretability theory and the realization mode of data storytelling, a data storytelling model of extraction-reorganization-narrative is constructed, and the data story mapping process is given by using the defined element tuple. The key techniques of story model design are introduced briefly. Result/Conclusion Under the theoretical guidance of data storytelling model design, this paper proposes a fan-shaped storytelling implementation path for interpretation results and an interactive framework that integrates the elements of interpretation results and storytelling model, and reflects the practical value of data storytelling method in result interpretation through case studies. A framework of data storytelling methods based on interpretable results is constructed, which provides new ideas for expanding storytelling paths with data perception and cognition and assisting intelligent decision-making.
Subjects: Library Science，Information Science >> Automation method and equipment in intelligence process submitted time 2023-01-13
Abstract：
In the era of knowledge economy, more and more enterprises begin to implement knowledge management to enhance their core competitiveness. However, how to find the business entry point of knowledge management implementation and quickly introduce knowledge management is often the most concerned problem of enterprises. In the era of knowledge economy, more and more enterprises begin to implement knowledge management to enhance their core competitiveness. However, how to find the business entry point of knowledge management implementation and quickly introduce knowledge management is often the most concerned problem of enterprises.
Peer Review Status:
Awaiting Review
Subjects: Library Science，Information Science >> Automation method and equipment in intelligence process submitted time 2022-08-31
Abstract： Objective In order to construct a event recognition model in historical books, the performance of sequence labeling method in event recognition in historical ancient books is compared with that of text generation method. Methods In this paper, "Three Kingdoms" is selected as the original corpus. To compare the performance of the two methods, performing on the "Three Kingdoms" event data set, the sequence labeling experiment used BMES annotation and builded the BBCN-SG model ,and the text generation experiment builded the T5-SG model.It also builded RoBERTa-SG and NEZHA-SG models to conduct comparative experiments on generative models. Combining three text generation models and integrating the idea of Stacking ensemble learning, the Stacking-TRN-SG model is constructed. Results On the subject of modeling event recognition in historical ancient books, the performance of the text generation method is significantly better than that of the sequence labeling method. In the text generation method, the performance of the three models is RoBERTa-SG > T5-SG > NEZHA-SG. Stacking ensemble learning greatly improves the recognition performance of generation models. Limitations The computational resources of this paper are limited, and the Stacking-TRN-SG model lacks application research in other historical and ancient corpora. Conclusions The Stacking-TRN-SG model constructed in this paper preliminarily realizes the automatic event recognition of historical ancient books.
Subjects: Computer Science >> Natural Language Understanding and Machine Translation Subjects: Library Science，Information Science >> Automation method and equipment in intelligence process submitted time 2019-10-29
Abstract： Purpose: Move recognition in scientific abstracts is an NLP task of classifying sentences of the abstracts into different types of language unit. To improve the performance of move recognition in scientific abstracts, a novel model of move recognition is proposed that outperforms BERT-Base method. Design: Prevalent models based on BERT for sentence classification often classify sentences without considering the context of the sentences. In this paper, inspired by the BERT's Masked Language Model (MLM), we propose a novel model called Masked Sentence Model that integrates the content and contextual information of the sentences in move recognition. Experiments are conducted on the benchmark dataset PubMed 20K RCT in three steps. And then compare our model with HSLN-RNN, BERT-Base and SciBERT using the same dataset. Findings: Compared with BERT-Base and SciBERT model, the F1 score of our model outperforms them by 4.96% and 4.34% respectively, which shows the feasibility and effectiveness of the novel model and the result of our model comes closest to the state-of-the-art results of HSLN-RNN at present. Research Limitations: The sequential features of move labels are not considered, which might be one of the reasons why HSLN-RNN has better performance. And our model is restricted to dealing with bio-medical English literature because we use dataset from PubMed which is a typical bio-medical database to fine-tune our model. Practical implications: The proposed model is better and simpler in identifying move structure in scientific abstracts, and is worthy for text classification experiments to capture contextual features of sentences. Originality: The study proposes a Masked Sentence Model based on BERT which takes account of the contextual features of the sentences in abstracts in a new way. And the performance of this classification model is significantly improved by rebuilding the input layer without changing the structure of neural networks.
Peer Review Status:Awaiting Review
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