Earticle

초록

영어

Background/Objectives: Information is crucial in present world; text is one form of information that is being exchanged in alarming rates. Natural language Processing is one field that concentrates on text analysis. Methods/Statistical analysis: Text Analyzers collects the word vectors and embed them into one by calculating semantics, and their relationships were considered on bases of dependencies and dependency trees which only targets subject to object relations and vice versa. In this digital era, microblogs involve more complicated text which are very hard using dependencies and relations to comprehend in bases of contextual semantics. Findings: In this paper we are addressing this problem by building a novel quantum enhanced modal. The proposed methodology exchange parameters between NLP algorithm and Quantum native optimizer allowing us to solve non-linear problems while composing the semantics. Improvements/Applications: We have integrated our methodology into a simple question and answering system for assessment, this system will give us the scores and answers build upon context already existing on the internet. In every Assessment Quantum Trained or Q-Trained algorithm exhibited promising results when compared with the best-in-class NLP algorithm ALBERT.

목차

Abstract
I. INTRODUCTION
II. GENERALIZED QUANTUM STRUCTURES
III. LITERATURE SURVEY
IV. QUANTUM MODELING
V. QUANTUM TRAINING
VI. CONCLUSION
ACKNOWLEDGMENT
REFERENCES

키워드

저자

• Y. Bharadwaj [ Masters Student, Department of Computer Science, K L University, Vaddeswaram, Andhra Pradesh, India ]
• K. Bhanu Prakash [ Professor, Department of Computer Science, K L University, Vaddeswaram, Andhra Pradesh, India ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  IJASC

• 간기

  계간

• pISSN

  2619-8150

• 수록기간

  2019~2024

• 십진분류

  KDC 327 DDC 332