Textual Information Processing Based on Multi-Dimensional Indicator Weights

Yuliang Yang; Zhengping Lin; Yuzhong Zhou; Jiahao Shi; Jie Lin

doi:10.4108/eetsis.3805

Authors

Yuliang Yang China Southern Power Grid (China)
Zhengping Lin China Southern Power Grid (China)
Yuzhong Zhou China Southern Power Grid (China)
Jiahao Shi China Southern Power Grid (China)
Jie Lin China Southern Power Grid (China)

DOI:

https://doi.org/10.4108/eetsis.3805

Keywords:

Textual information processing, semantic information, multi-dimensional indicator weights, artificial intelligence

Abstract

With the rapid advancement of artificial intelligence and wireless communication technologies, the abundance of textual information has grown significantly, accompanied by a plethora of multidimensional metrics such as innovation, application prospects, key technologies, and expected outcomes. Extracting valuable insights from these multifaceted indicators and establishing an effective composite evaluation weighting framework poses a pivotal challenge in text information processing. In response, we propose a novel approach in this paper to textual information processing, leveraging multi-dimensional indicator weights (MDIWs). Our method involves extracting semantic information from text and inputting it into an LSTM-based textual information processor (TIP) to generate MDIWs. These MDIWs are then processed to create a judgment matrix following by eigenvalue decomposition and normalization, capturing intricate semantic relationships. Our framework enhances the comprehension of multi-dimensional aspects within textual data, offering potential benefits in various applications such as sentiment analysis, information retrieval, and content summarization. Experimental results underscore the effectiveness of our approach in refining and utilizing MDIWs for improved understanding and decision-making. This work contributes to the enhancement of text information processing by offering a structured approach to address the complexity of multidimensional metric evaluation, thus enabling more accurate and informed decision-making in various domains.

References

A. E. Haddad and L. Najafizadeh, “The discriminative discrete basis problem: Definitions, algorithms, benchmarking, and application to brain’s functional dynamics,” IEEE Trans. Signal Process., vol. 71, pp. 1–16, 2023.

R. Gabrys, S. Pattabiraman, and O. Milenkovic, “Reconstruction of sets of strings from prefix/suffix compositions,” IEEE Trans. Commun., vol. 71, no. 1, pp. 3–12, 2023.

L. Liu, J. Zhang, S. Song, and K. B. Letaief, “Hierarchical federated learning with quantization: Convergence analysis and system design,” IEEE Trans. Wirel. Commun., vol. 22, no. 1, pp. 2–18, 2023.

T. Häckel, P. Meyer, F. Korf, and T. C. Schmidt, “Secure time-sensitive software-defined networking in vehicles,” IEEE Trans. Veh. Technol., vol. 72, no. 1, pp. 35–51, 2023.

Y. Song, Z. Gong, Y. Chen, and C. Li, “Tensorbased sparse bayesian learning with intra-dimension correlation,” IEEE Trans. Signal Process., vol. 71, pp. 31– 46, 2023.

H. Wan and A. Nosratinia, “Short-block length polarcoded modulation for the relay channel,” IEEE Trans. Commun., vol. 71, no. 1, pp. 26–39, 2023.

K. N. Ramamohan, S. P. Chepuri, D. F. Comesaña, and G. Leus, “Self-calibration of acoustic scalar and vector sensor arrays,” IEEE Trans. Signal Process., vol. 71, pp. 61–75, 2023.

Q. Lu, S. Li, B. Bai, and J. Yuan, “Spatially-coupled fasterthan- nyquist signaling: A joint solution to detection and code design,” IEEE Trans. Commun., vol. 71, no. 1, pp. 52–66, 2023.

B. Han, V. Sciancalepore, Y. Xu, D. Feng, and H. D. Schotten, “Impatient queuing for intelligent task offloading in multiaccess edge computing,” IEEE Trans. Wirel. Commun., vol. 22, no. 1, pp. 59–72, 2023.

H. Hou, Y. S. Han, P. P. C. Lee, Y. Wu, G. Han, and M. Blaum, “A generalization of array codes with local properties and efficient encoding/decoding,” IEEE Trans. Inf. Theory, vol. 69, no. 1, pp. 107–125, 2023.

X. Zhou, D. He, M. K. Khan, W. Wu, and K. R. Choo, “An efficient blockchain-based conditional privacy preserving authentication protocol for vanets,” IEEE Trans. Veh. Technol., vol. 72, no. 1, pp. 81–92, 2023.

D. Malak and M. Médard, “A distributed computationally aware quantizer design via hyper binning,” IEEE Trans. Signal Process., vol. 71, pp. 76–91, 2023.

Y. Xiong, S. Sun, L. Liu, Z. Zhang, and N. Wei, “Performance analysis and bit allocation of cell-free massive MIMO network with variable-resolution adcs,” IEEE Trans. Commun., vol. 71, no. 1, pp. 67–82, 2023.

Z. Feng, M. Yu, S. A. Evangelou, I. M. Jaimoukha, and D. Dini, “Mu-synthesis PID control of full-car with parallel active link suspension under variable payload,” IEEE Trans. Veh. Technol., vol. 72, no. 1, pp. 176–189, 2023.

S. Khirirat, X. Wang, S. Magnússon, and M. Johansson, “Improved step-size schedules for proximal noisy gradient methods,” IEEE Trans. Signal Process., vol. 71, pp. 189–201, 2023.

C. Chaieb, F. Abdelkefi, and W. Ajib, “Deep reinforcement learning for resource allocation in multi-band and hybrid OMA-NOMA wireless networks,” IEEE Trans. Commun., vol. 71, no. 1, pp. 187–198, 2023.

T. Gafni, B. Wolff, G. Revach, N. Shlezinger, and K. Cohen, “Anomaly search over discrete composite hypotheses in hierarchical statistical models,” IEEE Trans. Signal Process., vol. 71, pp. 202–217, 2023.

A. Gupta, M. Sellathurai, and T. Ratnarajah, “End-to-end learning-based full-duplex amplify-and-forward relay networks,” IEEE Trans. Commun., vol. 71, no. 1, pp. 199– 213, 2023.

H. Ma, Y. Fang, P. Chen, S. Mumtaz, and Y. Li, “A novel differential chaos shift keying scheme with multidimensional index modulation,” IEEE Trans. Wirel. Commun., vol. 22, no. 1, pp. 237–256, 2023.

B.Wang, “Internal boundary between entanglement and separability within a quantum state,” IEEE Trans. Inf. Theory, vol. 69, no. 1, pp. 251–261, 2023.

L. Hu, H. Li, P. Yi, J. Huang, M. Lin, and H. Wang, “Investigation on AEB key parameters for improving car to two-wheeler collision safety using in-depth traffic accident data,” IEEE Trans. Veh. Technol., vol. 72, no. 1, pp. 113–124, 2023.

Y. Liu, Z. Tan, A. W. H. Khong, and H. Liu, “An iterative implementation-based approach for joint source localization and association under multipath propagation environments,” IEEE Trans. Signal Process., vol. 71, pp. 121–135, 2023.

K. Ma, S. Du, H. Zou, W. Tian, Z. Wang, and S. Chen, “Deep learning assisted mmwave beam prediction for heterogeneous networks: A dual-band fusion approach,” IEEE Trans. Commun., vol. 71, no. 1, pp. 115–130, 2023.

Z. Zhang, Z. Shi, and Y. Gu, “Ziv-zakai bound for doas estimation,” IEEE Trans. Signal Process., vol. 71, pp. 136– 149, 2023.

S. Guo and X. Zhao, “Multi-agent deep reinforcement learning based transmission latency minimization for delay-sensitive cognitive satellite-uav networks,” IEEE Trans. Commun., vol. 71, no. 1, pp. 131–144, 2023.

X. Fang, W. Feng, Y. Wang, Y. Chen, N. Ge, Z. Ding, and H. Zhu, “Noma-based hybrid satellite-uav-terrestrial networks for 6g maritime coverage,” IEEE Trans. Wirel. Commun., vol. 22, no. 1, pp. 138–152, 2023.

R. Gabrys, V. Guruswami, J. L. Ribeiro, and K. Wu, “Beyond single-deletion correcting codes: Substitutions and transpositions,” IEEE Trans. Inf. Theory, vol. 69, no. 1, pp. 169–186, 2023.

S. Mosharafian and J. M. Velni, “Cooperative adaptive cruise control in a mixed-autonomy traffic system: A hybrid stochastic predictive approach incorporating lane change,” IEEE Trans. Veh. Technol., vol. 72, no. 1, pp. 136–148, 2023.

X. Niu and E. Wei, “Fedhybrid: A hybrid federated optimization method for heterogeneous clients,” IEEE Trans. Signal Process., vol. 71, pp. 150–163, 2023.

R. Yang, Z. Zhang, X. Zhang, C. Li, Y. Huang, and L. Yang, “Meta-learning for beam prediction in a dualband communication system,” IEEE Trans. Commun., vol. 71, no. 1, pp. 145–157, 2023.