Extracting Biographical Spatial Timelines: Corpus and Experiments

Vempala, Alakananda; Blanco, Eduardo

doi:10.1109/taslp.2020.2988418

Cited by 3 publications

(1 citation statement)

References 16 publications

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“…The login module ensures that users can log in to the system with legitimate identities and obtain the recording information for easy query and modification. First, the user fills in the account number and password as parameters and passes them to the server, which is compared with the information in the user information table [23]. If the authentication fails, an error message is displayed and the registration page is displayed.…”

Section: System Modulementioning

confidence: 99%

Research on English Vocabulary and Speech Corpus Recognition Based on Deep Learning

Zhen¹

2022

Wireless Communications and Mobile Computing

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In order to investigate how to recognize English words and speech corpus, an English vocabulary and English speech recognition model based on deep learning algorithm was proposed. Through recommending key technical problems and solutions based on deep learning algorithm, how to realize the recognition of English vocabulary and speech corpus was investigated. In the research, the accuracy of the method on the English vocabulary and speech corpus recognition based on the deep learning algorithm increased 79% over the previous methods. Combined with the principle of the deep automatic encoder and deep learning algorithm, the research emphasis was on the effects of speech recognition framework for speech corpus. The speech recognition research based on the theory of deep learning not only had a theoretical guidance meaning but also had the use value in the practical application.

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Section: System Modulementioning

confidence: 99%

Research on English Vocabulary and Speech Corpus Recognition Based on Deep Learning

Zhen¹

2022

Wireless Communications and Mobile Computing

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Topical Classification of Text Fragments Accounting for Their Nearest Context

Glazkova

2020

Autom Remote Control

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Construction of a Chinese Corpus for Multi-Type Economic Event Relation

Wan

Xiao

et al. 2022

ACM Trans. Asian Low-Resour. Lang. Inf. Process.

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We construct a Chinese Economic Event Treebank (CEETB), focusing on revealing economic and finance events and their relations. Investigating economic event relations will benefit academic research and practice in not just economics but many other scientific areas. The characteristics of economic-related texts (e.g., abundant longer enterprises names and terms) and the Chinese language speciality (e.g., component ellipsis in long sentences) have resulted in challenges in the event relation extraction task. Existing Chinese corpora containing economic event relations mainly focused on finance areas (e.g., the equity market) and only covered a few event types. To support research that may involve economic text analysis in Chinese, our CEETB is constructed following a carefully designed process. First, based on practical and research requirements, we summarize nine different types of event relations and four types of component ellipses in economic texts. Then, an excellent annotation scheme is presented to hyalinize the model, strategy, and process in annotation, followed by statistical analysis and quality evaluation for the CEETB corpus. Finally, to demonstrate the strengths of the constructed corpus in practical applications, we conduct experiments on five SOTA models for event relation extraction.

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Extracting Biographical Spatial Timelines: Corpus and Experiments

Cited by 3 publications

References 16 publications

Research on English Vocabulary and Speech Corpus Recognition Based on Deep Learning

Research on English Vocabulary and Speech Corpus Recognition Based on Deep Learning

Topical Classification of Text Fragments Accounting for Their Nearest Context

Construction of a Chinese Corpus for Multi-Type Economic Event Relation

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