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A survey of script learning Review

Yi Han, Linbo Qiao, Jianming Zheng, Hefeng Wu, Dongsheng Li, Xiangke Liao,hanyi12@nudt.edu.cn,qiao.linbo@nudt.edu.cn,zhengjianming12@nudt.edu.cn,wuhefeng@mail.sysu.edu.cn,dsli@nudt.edu.cn,xkliao@nudt.edu.cn

Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 3, Pages 287-436 doi: 10.1631/FITEE.2000347

Abstract: Script is the structured knowledge representation of prototypical real-life event sequences. Learning the commonsense knowledge inside the script can be helpful for machines in understanding natural language and drawing commonsensible inferences. is an interesting and promising research direction, in which a trained system can process narrative texts to capture script knowledge and draw inferences. However, there are currently no survey articles on , so we are providing this comprehensive survey to deeply investigate the standard framework and the major research topics on . This research field contains three main topics: event representations, models, and evaluation approaches. For each topic, we systematically summarize and categorize the existing systems, and carefully analyze and compare the advantages and disadvantages of the representative systems. We also discuss the current state of the research and possible future directions.

Keywords：脚本学习；自然语言处理；常识知识建模；事件推理

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Progress in Neural NLP: Modeling, Learning, and Reasoning Review

Ming Zhou, Nan Duan, Shujie Liu, Heung-Yeung Shum

Engineering 2020, Volume 6, Issue 3, Pages 275-290 doi: 10.1016/j.eng.2019.12.014

Abstract:

Natural language processing (NLP) is a subfield of artificial intelligence (AI) that focuses on enabling computers to understand and process human languages. In the last five years, we have witnessed the rapid development of NLP in tasks such as machine translation, question-answering, and machine reading comprehension based on deep learning and an enormous volume of annotated and unannotated data. In this paper, we will review the latest progress in the neural network-based NLP framework (neural NLP) from three perspectives: modeling, learning, and reasoning. In the modeling section, we will describe several fundamental neural network-based modeling paradigms, such as word embedding, sentence embedding, and sequence-to-sequence modeling, which are widely used in modern NLP engines. In the learning section, we will introduce widely used learning methods for NLP models, including supervised, semi-supervised, and unsupervised learning; multitask learning; transfer learning; and active learning. We view reasoning as a new and exciting direction for neural NLP, but it has yet to be well addressed. In the reasoning section, we will review reasoning mechanisms, including the knowledge, existing non-neural inference methods, and new neural inference methods. We emphasize the importance of reasoning in this paper because it is important for building interpretable and knowledge-driven neural NLP models to handle complex tasks. At the end of this paper, we will briefly outline our thoughts on the future directions of neural NLP.

Keywords： Natural language processing Deep learning Modeling learning and Reasoning

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ACM-2019第二届机器学习与自然语言处理国际会议

Conference Date: 20 Dec 2019

Conference Place: 海南海口三亚

Administered by: IAASE

Pre-Trained Language Models and Their Applications Review

Haifeng Wang, Jiwei Li, Hua Wu, Eduard Hovy, Yu Sun

Engineering 2023, Volume 25, Issue 6, Pages 51-65 doi: 10.1016/j.eng.2022.04.024

Abstract:

Pre-trained language models have achieved striking success in natural language processing (NLP), leading to a paradigm shift from supervised learning to pre-training followed by fine-tuning. The NLP community has witnessed a surge of research interest in improving pre-trained models. This article presents a comprehensive review of representative work and recent progress in the NLP field and introduces the taxonomy of pre-trained models. We first give a brief introduction of pre-trained models, followed by characteristic methods and frameworks. We then introduce and analyze the impact and challenges of pre-trained models and their downstream applications. Finally, we briefly conclude and address future research directions in this field.

Keywords： Pre-trained models Natural language processing

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Zhao Zhenjie: Natural Language Processing with Physical Common Sense (2020-11-6)

14 Oct 2022

Keywords：信息技术

第十六届自然语言处理青年学者研讨会（YSSNLP2019）

Conference Date: 3 May 2019

Conference Place: 中国/海南/琼海

Administered by: 中国中文信息学会青年工作委员会

Design and Application of Clinical Big Data Management System for Oncology

Ma Lin, Bao Chenlu, Li Qing,Wu Jingyi, Pan Hong’an, Li Pengfei, Zhang Luxia, Zhan Qimin

Strategic Study of CAE 2022, Volume 24, Issue 6, Pages 127-136 doi: 10.15302/J-SSCAE-2022.06.011

Abstract:

Cancer is a serious threat to human life and health. Along with the development of medical informatization in China, healthcare institutions have cumulated a great quantity of clinical data in oncology; however, these data have not been fully explored owing to the disunity of data standards and great difficulties in data management. Hence, establishing a national clinical big data management system for oncology based on artificial intelligence could potentially promote the application of clinical data in oncology, further improving the quality and efficiency of clinical management for oncology. This study conducted an in-depth analysis of the problems and challenges of clinical data management and application for oncology and presented the significant values of an oncology clinical data management system. Considering the complexity of multi-source and multi-modal data in oncology, we explored the possible mechanisms and pathways of applying artificial intelligence to the management and research of clinical data for oncology Furthermore, a full-circle solution was designed, and the construction framework and technology systems were promoted for the clinical data management system for oncology, including the development of common data models for oncology, data collection and security management, data standardization and structuring, data analysis and application, and data quality control. Besides, we validated the feasibility and benefits of the promoted system in clinical practice by taking the clinical data management for lung cancer in a tertiary hospital as an example. Finally, we proposed some suggestions on the research directions of the clinical big data management system for oncology.

Keywords： clinical big data management system oncology artificial intelligence common data model natural language processing

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Emerging topic identification from app reviews via adaptive online biterm topic modeling Research Article

Wan ZHOU, Yong WANG, Cuiyun GAO, Fei YANG,yongwang@ahpu.edu.cn

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 5, Pages 678-691 doi: 10.1631/FITEE.2100465

Abstract: Emerging topics in highlight the topics (e.g., software bugs) with which users are concerned during certain periods. Identifying emerging topics accurately, and in a timely manner, could help developers more effectively update apps. Methods for identifying emerging topics in based on s or clustering methods have been proposed in the literature. However, the accuracy of is reduced because reviews are short in length and offer limited information. To solve this problem, an improved (IETI) approach is proposed in this work. Specifically, we adopt techniques to reduce noisy data, and identify emerging topics in using the adaptive online biterm . Then we interpret the implicature of emerging topics through relevant phrases and sentences. We adopt the official app changelogs as ground truth, and evaluate IETI in six common apps. The experimental results indicate that IETI is more accurate than the baseline in identifying emerging topics, with improvements in the F1 score of 0.126 for phrase labels and 0.061 for sentence labels. Finally, we release the codes of IETI on Github (https://github.com/wanizhou/IETI).

Keywords： App reviews Emerging topic identification Topic model Natural language processing

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第五十七届国际计算语言学年会

Conference Date: 28 Jul 2019

Conference Place: 意大利/佛罗伦萨

Administered by: 国际计算语言学协会（ACL）

第五十七届国际计算语言学年会

Conference Date: 28 Jul 2019

Conference Place: 意大利/佛罗伦萨

Administered by: 国际计算语言学协会（ACL）

第十八届全国计算语言学会议(CCL2019)

Conference Date: 18 Oct 2019

Conference Place: 中国/云南/昆明

Administered by: 中国中文信息学会计算语言学专业委员会

Visual commonsense reasoning with directional visual connections Research Articles

Yahong Han, Aming Wu, Linchao Zhu, Yi Yang,yahong@tju.edu.cn

Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 5, Pages 615-766 doi: 10.1631/FITEE.2000722

Abstract: To boost research into cognition-level visual understanding, i.e., making an accurate inference based on a thorough understanding of visual details, (VCR) has been proposed. Compared with traditional visual question answering which requires models to select correct answers, VCR requires models to select not only the correct answers, but also the correct rationales. Recent research into human cognition has indicated that brain function or cognition can be considered as a global and dynamic integration of local neuron connectivity, which is helpful in solving specific cognition tasks. Inspired by this idea, we propose a to achieve VCR by dynamically reorganizing the that is contextualized using the meaning of questions and answers and leveraging the directional information to enhance the reasoning ability. Specifically, we first develop a GraphVLAD module to capture to fully model visual content correlations. Then, a contextualization process is proposed to fuse sentence representations with visual neuron representations. Finally, based on the output of , we propose to infer answers and rationales, which includes a ReasonVLAD module. Experimental results on the VCR dataset and visualization analysis demonstrate the effectiveness of our method.

Keywords：视觉常识推理；有向连接网络；视觉神经元连接；情景化连接；有向连接

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Indeterminacy Causal Inductive Automatic Reasoning Mechanism Based On Fuzzy State Describing

Yang Bingru,Tang Jing

Strategic Study of CAE 2000, Volume 2, Issue 5, Pages 44-50

Abstract:

New framework of knowledge representation of fuzzy language field and fuzzy language value structure is shown in this paper. Then the generalized cell automation that can synthetically process fuzzy indeterminacy and random indeterminacy and the generalized inductive logic causal model are brought forward. On this basis, the new logic indeterminate causal inductive automatic reasoning mechanism which is based on fuzzy state describing is brought forward. At the end of this paper its application in the development of intelligent controller is discussed.

Keywords： language field language value structure generalized cell automation generalized inductive logic causal model automatic reasoning intelligent controller

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Development and Prospect of Big Data Knowledge Engineering

Zheng Qinghua, Liu Huan, Gong Tieliang, Zhang Lingling, Liu Jun

Strategic Study of CAE 2023, Volume 25, Issue 2, Pages 208-220 doi: 10.15302/J-SSCAE-2023.02.018

Abstract:

Big Data Knowledge Engineering is the infrastructure of artificial intelligence, a common requirement faced by various industries and fields, and the inevitable path for the digitalization to intelligence. In this paper, we firstly elaborate on the background and connotation of big data knowledge engineering and propose a research framework of “data knowledgeization, knowledge systematization, and knowledge reasoning”. Secondly, we sort out the key technologies of knowledge acquisition and fusion, knowledge representation, and knowledge reasoning and introduce engineering applications in typical scenarios such as smart education, tax risk control, and smart healthcare. Thirdly, we summary the challenges faced by big data knowledge engineering and predict the future research directions including complex big data knowledge acquisition, knowledge+data hybrid learning, and brain-inspired knowledge coding and memorizing. Finally, several suggestions are given by the research: guiding interdisciplinary integration and establishing major and key R&D projects to promote the basic theory and technological breakthroughs of big data knowledge engineering; strengthening communication and cooperation between enterprises and research institutions as well as promoting cutting-edge research results to form application demonstrations, so as to establish an industry-standard system for big data knowledge engineering; exploring school-enterprise cooperation in line with market demands, orienting towards major application needs, and accelerating the landing application of big data knowledge engineering technology in the country's important industries.

Keywords： Big Data Knowledge Engineering Knowledge Acquisition Knowledge Fusion Knowledge Representation Knowledge Reasoning

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Technologies and Applications of Big Data Knowledge Engineering for Smart Taxation Systems

Zheng Qinghua , Shi Bin , Dong Bo

Strategic Study of CAE 2023, Volume 25, Issue 2, Pages 221-231 doi: 10.15302/J-SSCAE-2023.07.005

Abstract:

Taxation is vital for national governance, and the digital transformation of governments necessitates smart taxation. Therefore, analyzing the key issues and exploring the development ideas for smart taxation is of both theoretical and practical values. In this study, following an analysis of the development status and challenges facing China’s intelligent taxation field, we proposed a big data knowledge engineering approach that emphasizes data knowledgeization, knowledge systematization, and knowledge reasonability, and developed a five-layer technical architecture that consists of knowledge sources, knowledge extraction, knowledge mapping, knowledge reasoning, and application layers. After elaborating the representative application scenarios including knowledge-driven tax preference calculation, interpretable tax risk identification, intelligent decision support for tax policies, and smart tax questioning,we investigated the limitations of the proposed approach and further discussed the directions for future research. Furthermore, we proposed the following development suggestions in terms of data, technology, and ecology: (1) standardizing tax-related information and improving the national data sharing, opening, and guarantee system; (2) integrating the achievements of various information disciplines and improving the application system of big data knowledge engineering for smart taxation; and (3) promoting talent training and the development of technical standards for big data knowledge engineering.

Keywords： smart taxation knowledge engineering big data knowledge graph knowledge reasoning