A deep neural network model for coreference resolution in geological domain

Wan, Bo; Dong, Shuai; Chu, Deping; Li, Hong; Liu, Yiyang; Fu, Jinming; Fang, Fang; Li, Shengwen; Zhou, Dan

doi:10.1016/j.ipm.2023.103268

Cited by 11 publications

(2 citation statements)

References 37 publications

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“…Computer technology exhibits remarkable potential in the realm of interpretation, and intelligent information technology has revolutionized people’s lives ( Wan et al, 2023 ). This impact becomes even more significant in intensifying globalization, where effective communication between diverse languages is paramount.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy information recognition and translation processing in English interpretation based on a generalized maximum likelihood ratio algorithm

Yin

2024

PeerJ Computer Science

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English interpretation plays a vital role as a critical link in cross-language communication. However, there are various types of ambiguous information in many interpreting scenarios, such as ambiguity, ambiguous vocabulary, and syntactic structures, which may lead to inaccuracies and fluency issues in translation. This article proposes a method based on the generalized maximum likelihood ratio algorithm (GLR) to identify and process fuzzy information in English interpretation to improve the quality and efficiency of performance. Firstly, we systematically analyzed the common types of fuzzy information in interpretation and delved into the basic principles and applications of the generalized maximum likelihood ratio algorithm. This algorithm is widely used in natural language processing to solve uncertainty problems and has robust modeling and inference capabilities, making it suitable for handling fuzzy information in interpretation. Then, we propose a fuzzy information recognition model based on the generalized maximum likelihood ratio algorithm. This model utilizes a large-scale interpretation corpus for training and identifies potential fuzzy information in the interpretation process through statistical analysis and pattern recognition. Once fuzzy information is detected, we adopt a series of effective translation processing strategies, including contextual inference and adaptation, to ensure the accuracy and naturalness of interpretation. Finally, we conducted a series of experiments to evaluate the performance of the proposed method. The experimental results show that the fuzzy information recognition and translation processing method based on the generalized maximum likelihood ratio algorithm performs well in different interpretation scenarios, significantly improving the quality and fluency of interpretation and reducing ambiguity caused by fuzzy information.

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Section: Introductionmentioning

confidence: 99%

Fuzzy information recognition and translation processing in English interpretation based on a generalized maximum likelihood ratio algorithm

Yin

2024

PeerJ Computer Science

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“…In the study of interpretation, computer technology has great potential. Intelligent information technology has brought great changes to people's lives (Wan et al 2023). Especially with the intensification of globalization, communication between different languages has become particularly important.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Information Recognition and Translation Processing in English Interpretation based on Artificial Intelligence Recognition Technology

Li¹

2023

Preprint

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Interpretation is an oral expression that converts the information heard and understood in the source language into the target language quickly and accurately, thus completing the role of information transmission. However, language contains too much fuzzy information, so it is inevitable to have fuzzy information in interpretation. The characteristics of fuzzy information, the differences between different languages and cultural backgrounds, and the unpredictability of interpretation have brought great challenges to interpretation. This paper proposes an improved generalized maximum likelihood ratio algorithm (GLR) for fuzzy information processing in English. To improve interpretation accuracy, this study analyzes the characteristics of language databases, vocabulary, grammar, and translation. More specifically, the principle of natural language processing research via intelligent recognition technology is introduced in this study. Secondly, the author introduces the role of vague language in oral communication. Then, this paper introduces the fuzzy language processing method via the improved GLR method in detail. Finally, the experimental results are given to verify the effectiveness of the method.

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Integrated framework for geological modeling: integration of data, knowledge, and methods

Li,

Wan,

Chu

et al. 2024

Bull Eng Geol Environ

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A deep neural network model for coreference resolution in geological domain

Cited by 11 publications

References 37 publications

Fuzzy information recognition and translation processing in English interpretation based on a generalized maximum likelihood ratio algorithm

Fuzzy information recognition and translation processing in English interpretation based on a generalized maximum likelihood ratio algorithm

Fuzzy Information Recognition and Translation Processing in English Interpretation based on Artificial Intelligence Recognition Technology

Integrated framework for geological modeling: integration of data, knowledge, and methods

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