Performance Evaluation of Keyword Extraction Methods and Visualization for Student Online Comments

Liu, Feng; Huang, Xiaodi; Huang, Weidong; Duan, Sophia Xiaoxia

doi:10.3390/sym12111923

Cited by 11 publications

(3 citation statements)

References 61 publications

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“…In addition to our fuzzy logic-based degree of relevance calculation approach, we employ the following metrics [37] to evaluate the accuracy of our generated indexes using our new approach. The results of these measurements remain relative because the number of words extracted and the nature and length of the document affect them.…”

Section: Evaluation Metricsmentioning

confidence: 99%

A novel fuzzy logic-based approach for textual documents indexing

Rassam,

Ettahiri,

Zellou

et al. 2024

IJEECS

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In the evolving landscape of information retrieval and natural language processing, the quest for more effective automatic keyword extraction (AKE) techniques from textual documents has become a pivotal research focus. Existing methodologies, while offering valuable insights, often grapple with the challenges posed by the imprecision and variability inherent in human language. This has led to a growing recognition of the need for innovative approaches to navigating textual content’s nuances more adeptly. In response to this imperative, this paper proposes a novel fuzzy indexing approach designed specifically for the indexing of textual documents. Fuzzy indexing, grounded in the principles of fuzzy logic, provides solutions for handling the inherent uncertainty and imprecision in natural language, especially when confronted with the intricacies of linguistic ambiguity and variability. By leveraging the power of fuzzy logic, we aim to enhance the precision of keyword extraction. This paper unfolds the intricacies of our fuzzy indexing approach, detailing the theoretical methodology through empirical evaluation and comparative analysis; we seek to demonstrate the efficacy of our approach in outperforming traditional methods in the context of fuzzy indexing for textual documents.

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Section: Evaluation Metricsmentioning

confidence: 99%

A novel fuzzy logic-based approach for textual documents indexing

Rassam,

Ettahiri,

Zellou

et al. 2024

IJEECS

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“…Although several metrics are available to evaluate the performance of AKE methods [47]. However, most researchers prefer to use only three measures, recall (5) which expresses the number of keyphrases extracted from among the keyphrases of the document.…”

Section: Evaluation Metricsmentioning

confidence: 99%

Present and absent keyphrases extraction: an approach based on sentence embedding

Ajallouda

Zellou

2022

IJEECS

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The automatic keyphrases extraction (AKE) of a document is any expression by which we can learn its content without having to read it. Keyphrases are exploited in natural language processing (NLP) applications. These phrases are often mentioned in the document but there may be some keyphrases that are not mentioned. In the field of AKE, researchers have exploited many techniques, such as statistical calculation, deep learning algorithms, graph representation, and sentence embedding techniques. Approaches that exploit embedding techniques calculate the similarity between a document and a candidate keyphrase, where similar phrases to the document are considered as keyphrases. Representing the document by a single vector makes its performance poor, especially in long documents. This is in addition to the inability of these methods to generate absent keyphrases. In order to overcome these problems, our paper proposes an unsupervised approach to AKE, based on the universal sentence encoder (USE) to represent candidate keyphrases and parts of the document probably containing keyphrases. Our method also generates keyphrases not mentioned in the text. We compared the performance of the proposed approach with other methods based on embedding techniques, where the results showed the superiority of our approach especially in long documents.

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“…In the current environment, where countless documents are published through various media [5,6], it becomes time-consuming for humans to process all documents and identify their keywords. Consequently, the demand for automatic keyword extraction has increased [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

RoBERTa-Based Keyword Extraction from Small Number of Korean Documents

Kim,

Lee,

Park

et al. 2023

Electronics

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Keyword extraction is the task of identifying essential words in a lengthy document. This process is primarily executed through supervised keyword extraction. In instances where the dataset is limited in size, a classification-based approach is typically employed. Therefore, this paper introduces a novel keyword extractor based on a classification approach. The proposed keyword extractor comprises three key components: RoBERTa, a keyword estimator, and a decision rule. RoBERTa encodes an input document, the keyword estimator calculates the probability of each token in the document becoming a keyword, and the decision rule ultimately determines whether each token is a keyword based on these probabilities. However, training the proposed model with a small dataset presents two challenges. One problem is the case that all tokens in the documents are not a keyword, and the other problem is that a single word can be composed of keyword tokens and non-keyword tokens. Two novel heuristics are thus proposed to tackle these problems. To address these issues, two novel heuristics are proposed. These heuristics have been extensively tested through experiments, demonstrating that the proposed keyword extractor surpasses both the generation-based approach and the vanilla RoBERTa in environments with limited data. The efficacy of the heuristics is further validated through an ablation study. In summary, the proposed heuristics have proven to be effective in developing a supervised keyword extractor with a small dataset.

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Performance Evaluation of Keyword Extraction Methods and Visualization for Student Online Comments

Cited by 11 publications

References 61 publications

A novel fuzzy logic-based approach for textual documents indexing

A novel fuzzy logic-based approach for textual documents indexing

Present and absent keyphrases extraction: an approach based on sentence embedding

RoBERTa-Based Keyword Extraction from Small Number of Korean Documents

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