Fuzzy Logic-based N-gram Graph Technique for Evaluating Textual Documents Indexes

Rassam, Latifa; Aldiebesghanem, Chaimae; Zellou, Ahmed; Lahmar, Elhabib Ben

doi:10.1109/iccci55554.2022.9850268

Cited by 1 publication

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“…This function calculates the count of occurrences of the expression x within the specific textual document td. By applying this function, we can determine the degree to which expression 𝑥 𝑖 is present or relevant within 𝑡𝑑 1 [32]:…”

Section: Quantifying N-gram Word Occurrence Using Our Fuzzy Logic App...mentioning

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: Quantifying N-gram Word Occurrence Using Our Fuzzy Logic App...mentioning

confidence: 99%