A Review on Question Analysis, Document Retrieval and Answer Extraction Method in Question Answering System

Thalib, Irfandy; Widyawan,; Soesanti, Indah

doi:10.1109/icosta48221.2020.1570614175

Cited by 8 publications

(2 citation statements)

References 24 publications

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“…The study recognised that semantic and syntactic extraction are important for getting accurate results with SVM classifiers in information retrieval and question-answering systems, but it noted the relatively lower performance in educational settings. The results of [15] show that using NLP features like lexical and semantic matching along with machine learning methods like SVM makes question-answering systems better at classifying things. However, the study highlighted the substantial impact of the dataset's domain quality on the machine learning baseline, underscoring the need for further research into cross-domain machine learning applications.…”

Section: Literature In Reviewmentioning

confidence: 99%

A Review on NLP Techniques and Associated Challenges in Extracting Features from Education Data

Ahidi Elisante Lukwaro,

Kalegele,

G. Nyambo

2024

IJCDS

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There has been a significant increase in academic processes to ensure the quality of educational resources such as curricula, examinations, and educational content. This has drawn attention to studies exploring the use of text mining, learning machines, and auto-analytic tools like natural language processing (NLP) to interpret and evaluate the quality of these educational resources. Auto-analytical techniques are required to evaluate the quality of educational content; otherwise, manual evaluation can be burdensome and improperly influenced by human instincts. This study employs a methodical approach to comprehensively survey NLP techniques for extracting syntactic and semantic features to analyze and comprehend educational content. NLP, in combination with machine learning, is an ideal tool for automatically evaluating the aspects of higher education quality. This is because they include features that aid in textual content comprehension as well as implementing natural language techniques that provide an interpretive interface between humans and machines. The review highlights the limitations of NLP in evaluating educational data, including the need for sentence-level understanding and the need for research to address challenges like noise in text data, domain-specific language variations, and improving model robustness for effective feature extraction in educational contexts. The findings of this review hold substantial benefits for various stakeholders, including education regulatory bodies, researchers, higher education institutions, and NLP researchers. Notably, the study equips NLP researchers with valuable insights into document analysis's current strengths and weaknesses. The accumulated evidence can provide the skills to develop NLP-based applications for evaluating the relevant and quality aspects of education in higher educational settings. Furthermore, NLP researchers can be updated on the strengths and limitations of document analysis, allowing them to apply effective text representation approaches and implement the appropriate algorithm and techniques for NLP tasks, particularly in educational data.

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Section: Literature In Reviewmentioning

confidence: 99%

A Review on NLP Techniques and Associated Challenges in Extracting Features from Education Data

Ahidi Elisante Lukwaro,

Kalegele,

G. Nyambo

2024

IJCDS

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“…Soesanti et. al in [10] presented a literature review analyzed the state-of-the-art methods used by Question answering systems in question analysis, document retrieval and answer extraction stages in recent years.…”

Section: )mentioning

confidence: 99%

Surveying Question Answering Systems: A Comparative Study

Sabry

Sauber

Elkafrawy

2022

The Egyptian Journal of Language Engineering

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Question Answering (QA) systems are considered an advanced form of information retrieval that enables asking specific questions by humans and the system infer answers using natural language queries with deeper knowledge understanding mechanisms from huge amount of data instead of having the user search all documents himself. As "what a user really wants" is often a precise answer to a question by consulting documents on the web or using special knowledge base. Although many systems have been developed over the past years, it remains a challenge that most systems yet require improvements to increase the accuracy for correct interpretation of the question and provide a good exact answer to user questions. This paper presents a short study of the generic QA framework and aims to survey some of the current state-of-the-art Question Answering systems in different domains and makes a comparison between all of these systems based on some identified criteria in literature.

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Systematic Literature Review of Question Answering Systems

Mattila

Dahanayke

2021

Lecture Notes in Networks and Systems

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A Review on Question Analysis, Document Retrieval and Answer Extraction Method in Question Answering System

Cited by 8 publications

References 24 publications

A Review on NLP Techniques and Associated Challenges in Extracting Features from Education Data

A Review on NLP Techniques and Associated Challenges in Extracting Features from Education Data

Surveying Question Answering Systems: A Comparative Study

Systematic Literature Review of Question Answering Systems

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