DeepKE: A Deep Learning Based Knowledge Extraction Toolkit for Knowledge Base Population

Zhang, Ningyu; Xu, Xin; Tao, Liankuan; Yu, Haiyang; Ye, Hongbin; Qiao, Shuofei; Xie, Xin; Chen, Xiang; Li, Zhoubo; Li, Lei

doi:10.18653/v1/2022.emnlp-demos.10

Cited by 16 publications

(1 citation statement)

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“…The Chinese word segmentation tool jieba is used for data set Chinese word segmentation. To avoid splitting certain movie and song names, a dictionary is built based on the data crawled in the second part; the named entity recognition function provided by the language technology platform [19] (LTP) is used; and the Chinese relation extraction open-source tool DeepK [20], developed by the Knowledge Engine Laboratory of Zhejiang University, is used to parse text.…”

Section: Data Preprocessingmentioning

confidence: 99%

Design of Artificial Intelligence Companion Chatbot

Chen,

Kang,

2024

JNM

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With the development of cities and the prevalence of networks, interpersonal relationships have become increasingly distant. When people crave communication, they hope to find someone to confide in. With the rapid advancement of deep learning and big data technologies, an enabling environment has been established for the development of intelligent chatbot systems. By effectively combining cutting-edge technologies with humancentered design principles, chatbots hold the potential to revolutionize our lives and alleviate feelings of loneliness. A multi-topic chat companion robot based on a state machine has been proposed, which can engage in fluent dialogue with humans and meet different functional requirements. It can chat with users about movies, music, and other related topics, and recommend movies and music that may interest them to alleviate their loneliness and provide companionship. The interaction platform of the companion robot is realized through the QQ communication platform, with two chat modes: Conversation mode and recommendation mode. First, the KdConv open-source corpus was selected, and Python was used to crawl information on movies and music from Douban and QQ Music to establish and pre-process the dataset. Then, the dialogue function was implemented using generative language models and retrieval systems, while the recommendation function was achieved using user profiling and collaborative filtering. Finally, a state machine algorithm was used to achieve real-time switching between the two chat modes of the companion robot. In conclusion, test participants gave high ratings for the accuracy of the companion robot's responses and the satisfaction with its content recommendations. Compared to traditional large-scale integrated models, this robot employs a state-machine framework to achieve diverse functions through seamless state transitions, thereby enhancing computational speed and precision. Additionally, the robot can recommend movies and music, providing companionship and alleviating loneliness for users, which is of great significance in modern society where interpersonal relationships are increasingly alienated.

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Section: Data Preprocessingmentioning

confidence: 99%

Design of Artificial Intelligence Companion Chatbot

Chen,

Kang,

2024

JNM

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Cross-Modal Knowledge Discovery, Inference, and Challenges

Wang

Zhang

2023

Lecture Notes in Computer Science

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Dynamic Multi-View Fusion Mechanism for Chinese Relation Extraction

Yang

et al. 2023

Lecture Notes in Computer Science

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Recently, many studies incorporate external knowledge into character-level feature based models to improve the performance of Chinese relation extraction. However, these methods tend to ignore the internal information of the Chinese character and cannot filter out the noisy information of external knowledge. To address these issues, we propose a mixture-of-view-experts framework (MoVE) to dynamically learn multi-view features for Chinese relation extraction. With both the internal and external knowledge of Chinese characters, our framework can better capture the semantic information of Chinese characters. To demonstrate the effectiveness of the proposed framework, we conduct extensive experiments on three real-world datasets in distinct domains. Experimental results show consistent and significant superiority and robustness of our proposed framework. Our code and dataset will be released at: https://gitee.com/tmg-nudt/multi-view-of-expert-for-chinese-relation-extraction

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DeepKE: A Deep Learning Based Knowledge Extraction Toolkit for Knowledge Base Population

Cited by 16 publications

References 0 publications

Design of Artificial Intelligence Companion Chatbot

Design of Artificial Intelligence Companion Chatbot

Cross-Modal Knowledge Discovery, Inference, and Challenges

Dynamic Multi-View Fusion Mechanism for Chinese Relation Extraction

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