Entity extraction based on the combination of information entropy and TF-IDF

Yilahun, Hankiz; Hamdulla, Askar

doi:10.1504/ijris.2023.128371

Cited by 12 publications

(7 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Applying TF-IDF and WordVec to the construction of knowledge graphs can significantly enhance the information retrieval capabilities and semantic parsing efficiency of knowledge graphs [69,70]. Using TF-IDF effectively identifies keywords or phrases that often carry crucial information linking different entities and attributes [71]. For example, when building a knowledge graph of legal documents, TF-IDF can help extract key legal terms and related definitions, which are fundamental to constructing entities and relationships.…”

Section: Dataset Preprocessing 321 Standard Preprocessing Methodsmentioning

confidence: 99%

Data-Driven Strategies for Complex System Forecasts: The Role of Textual Big Data and State-Space Transformers in Decision Support

Huo,

Guo,

Yang

et al. 2024

Systems

View full text Add to dashboard Cite

In this research, an innovative state space-based Transformer model is proposed to address the challenges of complex system prediction tasks. By integrating state space theory, the model aims to enhance the capability to capture dynamic changes in complex data, thereby improving the accuracy and robustness of prediction tasks. Extensive experimental validations were conducted on three representative tasks, including legal case judgment, legal case translation, and financial data analysis to assess the performance and application potential of the model. The experimental results demonstrate significant performance improvements of the proposed model over traditional Transformer models and other advanced variants such as Bidirectional Encoder Representation from Transformers (BERT) and Finsformer across all evaluated tasks. Specifically, in the task of legal case judgment, the proposed model exhibited a precision of 0.93, a recall of 0.90, and an accuracy of 0.91, significantly surpassing the traditional Transformer model (with precision of 0.78, recall of 0.73, accuracy of 0.76) and performances of other comparative models. In the task of legal case translation, the precision of the proposed model reached 0.95, with a recall of 0.91 and an accuracy of 0.93, also outperforming other models. Likewise, in the task of financial data analysis, the proposed model also demonstrated excellent performance, with a precision of 0.94, recall of 0.90, and accuracy of 0.92. The state space-based Transformer model proposed not only theoretically expands the research boundaries of deep learning models in complex system prediction but also validates its efficiency and broad application prospects through experiments. These achievements provide new insights and directions for future research and development of deep learning models, especially in tasks requiring the understanding and prediction of complex system dynamics.

show abstract

Section: Dataset Preprocessing 321 Standard Preprocessing Methodsmentioning

confidence: 99%

Data-Driven Strategies for Complex System Forecasts: The Role of Textual Big Data and State-Space Transformers in Decision Support

Huo,

Guo,

Yang

et al. 2024

Systems

View full text Add to dashboard Cite

show abstract

“…TF-IDF method is a commonly used weighting technique [17]. Where TF is the frequency of words appearing in the text, i.e., the word, the greater the frequency indicates the greater the contribution of the word to the text: IDF is the inverted text frequency, which refers to the distribution of the word in the whole set of text, if the word is rarely found in other texts, it indicates that the word has a very good ability to distinguish between categories.…”

Section: Tf-idf Methodologymentioning

confidence: 99%

Research on the Path of Enhancing Students’ Political Identity in Teaching Ethics and Rule of Law in the Information Age

Zhang,

Bian

2024

Applied Mathematics and Nonlinear Sciences

View full text Add to dashboard Cite

Political identity directly affects the implementation of the nurturing function of politics and the enhancement of the teaching effect, this paper proposes a path to enhance students’ political identity in the teaching of Daofa based on the multimodal fusion model. The TF-IDF method is utilized to identify political emotions contained in students’ classroom expressions, and LBP is used to extract students’ facial expression features. Based on the above-extracted results, a multimodal feature learner emotion data model is designed, which is also combined with a deep RNN model to identify and analyze students’ learning behaviors in the actual Daofa classroom. Evaluating students’ political identity from five dimensions, the results show that all students have significant differences in the five dimensions, P<0.05, and their political identity is above 60, of which the highest political identity in the fourth year of college reaches 78.586. Among the states of political identity, the state of maturity type has the highest frequency of occurrence, which is 132 times. The results of the study provide data support for improving students’ political identity.

show abstract

“…The system adopts a CIS structure, with servers deployed in the ship software status command room, and clients distributed on all ship stations that require detection and maintenance [17]. The server has a visual interface and complete functions such as ship and ship software status detection, fault software repair, software version release and update; The client does not have a visual interface and is a console program that starts automatically in the background.…”

Section: Deployment Designmentioning

confidence: 99%

Intelligent Detection and Analysis of Software Vulnerabilities based on Encryption Algorithms and Feature Extraction

Li,

Wei

2024

SCPE

View full text Add to dashboard Cite

Implement status detection of ship software, identify the source of faults in problematic software, and release new software versions. Based on the above requirements, the author regards the detection and control of ship software status as the core research content. Based on the actual operating environment of ship software, the functional requirements of software status detection were studied and analyzed, and a set of ship software status detection was designed and implemented, a software inspection and maintenance platform that integrates ship software operation and maintenance, as well as ship software version release and update. The author conducted practical verification of the SM3 and SM2 hybrid encryption algorithm and selected software on the ship for detection. After analyzing the experimental results, it has been proven that using a hybrid algorithm for encryption and decryption, the server can accurately obtain software information on the ship's platform, detect the software status on the ship, and locate specific problem files. For software that does not meet the standard status, the server can accurately transmit software information to the ``component integration framework'' and put the component in a ``prohibited'' scheduling state. After the server repairs the problematic software, the detection results of the software change and display as legal, while the software is in the ``allowed'' scheduling state in the ``component integration framework''.

show abstract

Entity extraction based on the combination of information entropy and TF-IDF

Cited by 12 publications

References 0 publications

Data-Driven Strategies for Complex System Forecasts: The Role of Textual Big Data and State-Space Transformers in Decision Support

Data-Driven Strategies for Complex System Forecasts: The Role of Textual Big Data and State-Space Transformers in Decision Support

Research on the Path of Enhancing Students’ Political Identity in Teaching Ethics and Rule of Law in the Information Age

Intelligent Detection and Analysis of Software Vulnerabilities based on Encryption Algorithms and Feature Extraction

Contact Info

Product

Resources

About