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To obtain professional entity information including expert research fields in academic papers and provide theoretical references for academic paper or technology project review experts, an entity recognition model based on RoBERTa-wwm is proposed to identify professional entities in academic papers in the field of computer science. First, with the reference of the available experts' basic information table, the abstract data of these experts' academic papers are obtained through the advanced search of the China National Knowledge Infrastructure ( CNKI) and crawler technology. Next, the abstract data are manually annotated and the RoBERTa-wwm pre-training model is employed to obtain character vectors with semantic features as inputs for downstream models. Finally, the semantic character vectors are put into the BiLSTM-CRF model to identify professional entity recognition in the text. The experiments show the proposed model achieves better results in the self-labeled dataset. The F1 score of model reaches 89.94%, higher than all other comparison models in the experiment, demonstrating its excellent ability to identify professional entities. [ABSTRACT FROM AUTHOR]

In recent decades, causal inference has been a significant research topic in various fields, including statistics, computer science, education, public policy, and economics. Most causal inference methods focus on the analysis of sample observational data and text corpora. However, with the emergence of various knowledge graphs and large language models, causal inference tailored to knowledge graphs and large models has gradually become a research hotspot. In this paper, different causal inference methods are classified based on their orientation towards sample observational data, text data, knowledge graphs, and large language models. Within each classification, this paper provides a detailed analysis of classical research works, including their problem definitions, solution methods, contributions, and limitations. Additionally, this paper places particular emphasis on discussing recent advancements in the integration of causal inference methods with knowledge graphs and large language models. Various causal inference methods are analyzed and compared from the perspectives of efficiency and cost, and specific applications of knowledge graphs and large language models in causal inference tasks are summarized. Finally, future development directions of causal inference in combination with knowledge graphs and large models are prospected. [ABSTRACT FROM AUTHOR]

Copyright of Society: Chinese Journal of Sociology / Shehui is the property of Society: Chinese Journal of Sociology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Guangdong Architecture Civil Engineering is the property of Guangdong Architecture Civil Engineering Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Image & Graphics is the property of Editorial Office of Journal of Image & Graphics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Based on the theory of conceptual metaphor, this paper discusses the formation and characteristics of conceptual metaphor in the computer field and its influence on human thinking. The functions of the human brain and the expressions and related concepts used to describe these functions are applied to the computer, thus conceptual metaphor concerning the computer come into being. Conceptual metaphor in the field of computer, which is not only of universality but also of systematization, reflects human’s cognition of the computer and also influences human’s thinking mode to cognize the world. The study of this kind of conceptual metaphor can deepen our understanding of computer science and widen the research field of metaphor cognitive function as well. [ABSTRACT FROM AUTHOR]

Practice teaching is an essential link of university education, and provides a powerful support of academic teaching. Building outside-school practice bases and further improving university-enterprise cooperation patterns of cooperative education are effective approaches for training the practice ability, and strongly guarantee the employment and entrepreneurship for students. In this paper, we summarize the founding process of outside-school practice bases of School of Computer Science and Engineering of Dalian Minzu University, analyze the problems existing in outside-school practice teaching, and propose a method for further deepening practice teaching, which has certain reference significance on strengthening the cooperative education and improving the practice training ability of students in universities. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Education Research (1680-6360) is the property of Angle Publishing Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)