Named Entity Recognition of Ancient Poems Based on Albert-BiLSTM-MHA-CRF Model

Zhou, Faguo; Wang, Chao; Wang, Jipeng

doi:10.1155/2022/6507719

Cited by 3 publications

(2 citation statements)

References 32 publications

(29 reference statements)

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“…Aurpa TT applied a deep neural network model based on Transformer to accurately and swiftly obtain answers for reading comprehension in the Bengali language [34]. Zhou, FG et al introduced the Albert-BiLSTM-MHA-CRF model for extracting and constructing KG related to ancient poetry entities, exploring the connections between ancient poems to inherit Chinese traditional culture [35].…”

Section: Application Of Kg In the Field Of Ichmentioning

confidence: 99%

Construction and application of a knowledge graph-based question answering system for Nanjing Yunjin digital resources

Xu,

Lu,

Liu

2023

Herit Sci

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Nanjing Yunjin, one of China's traditional silk weaving techniques, is renowned for its unique local characteristics and exquisite craftsmanship, and was included in the Representative List of the Intangible Cultural Heritage of Humanity by UNESCO in 2009. However, with rapid development in weaving technology, ever-changing market demands, and shifting public aesthetics, Nanjing Yunjin, as an intangible cultural heritage, faces the challenge of survival and inheritance. Addressing this issue requires efficient storage, management, and utilization of Yunjin knowledge to enhance public understanding and recognition of Yunjin culture. In this study, we have constructed an intelligent question-answering system for Nanjing Yunjin digital resources based on knowledge graph, utilizing the Neo4j graph database for efficient organization, storage, and protection of Nanjing Yunjin knowledge, thereby revealing its profound cultural connotations. Furthermore, we adopted deep learning algorithms for natural language parsing. Specifically, we adopted BERT-based intent recognition technology to categorize user queries by intent, and we employed the BERT + BiGRU + CRF model for entity recognition. By comparing with BERT + BILSTM + CRF, BERT + CRF and BILSTM + CRF models, our model demonstrated superior performance in terms of precision, recall, and F1 score, substantiating the superiority and effectiveness of this model. Finally, based on the parsed results of the question, we constructed knowledge graph query statements, executed by the Cypher language, and the processed query results were fed back to the users in natural language. Through system implementation and testing, multiple indices including system response time, stability, load condition, accuracy, and scalability were evaluated. The experimental results indicated that the Nanjing Yunjin intelligent question-answering system, built on the knowledge graph, is able to efficiently and accurately generate answers to user’s natural language queries, greatly facilitating the retrieval and utilization of Yunjin knowledge. This not only reinforces the transmission, promotion, and application of Yunjin culture but also provides a paradigm for constructing other intangible cultural heritage question-answering systems based on knowledge graphs. This has substantial theoretical and practical significance for deeply exploring and uncovering the knowledge structure of human intangible heritage, promoting cultural inheritance and protection.

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Section: Application Of Kg In the Field Of Ichmentioning

confidence: 99%

Construction and application of a knowledge graph-based question answering system for Nanjing Yunjin digital resources

Xu,

Lu,

Liu

2023

Herit Sci

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“…Named entity recognition is a critical task that can help researchers better understand the background and events in ancient literary works, such as the names of people, places, and imagery (Zhao et al, 2022; Zhou et al, 2022). In particular, imagery, as a highly artistic means of expression, referring to something other than themselves, has been used to express profound thoughts and emotions concisely (Yu, 1981).…”

Section: Related Workmentioning

confidence: 99%

The Spatial‐Temporal Trajectories of the Poets and the Sociocultural Environment of the Age

Zhan,

Wang

2023

Proceedings of the Association for Information Science and Tech

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This research in progress presents a deep learning‐based approach to identifying named entities, including geographical locations and poetic imagery in ancient Chinese poetry. By leveraging association rule mining, this study establishes a connection between historical events, spatial‐temporal trajectories of poets, and sociocultural phenomena of the age. From the perspective of digital humanities, this study hopes to be able to provide new evidence for socioeconomic status, cultural openness, or historical events at different ages and to picture a better view of the development of classical Chinese.

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A Novel Named Entity Recognition Mehod for Bus Route Identification in Social Media

Shou,

2023

2023 2nd International Conference on Machine Learning, Cloud Computing and Intelligent Mining (MLCCIM)

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Named Entity Recognition of Ancient Poems Based on Albert-BiLSTM-MHA-CRF Model

Cited by 3 publications

References 32 publications

Construction and application of a knowledge graph-based question answering system for Nanjing Yunjin digital resources

Construction and application of a knowledge graph-based question answering system for Nanjing Yunjin digital resources

The Spatial‐Temporal Trajectories of the Poets and the Sociocultural Environment of the Age

A Novel Named Entity Recognition Mehod for Bus Route Identification in Social Media

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