Text Concepts Extraction based on Arabic WordNet and Formal Concept Analysis

Bouhriz, Nadia; Benabbou, Faouzia; Benlahmer, Habib

doi:10.5120/19625-1538

Cited by 2 publications

(1 citation statement)

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“…Maximizing the semantic similarity between the query word and the retrieved term improves the precision [81]. Bölücü and Can [31] presents a joint PoS tagging and stemming model that integrates two Hidden Markov Models.…”

Section: Context-sensitive Approachesmentioning

confidence: 99%

An Analytical Analysis of Text Stemming Methodologies in Information Retrieval and Natural Language Processing Systems

Jabbar,

Iqbal,

Tamimy

et al. 2023

IEEE Access

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The exponential increase in textual unstructured digital data creates significant demand for advanced and smart stemming systems. As a preprocessing stage, stemming is applied in various research fields such as information retrieval (IR), domain vocabulary analysis, and feature reduction in many natural language processing (NLP). Text stemming (TS), an important step, can significantly improve performance in such systems. Text-stemming methods developed till now could be better in their results and can produce errors of different types leading to degraded performance of the applications in which these are used. This work presents a systematic study with an in-depth review of selected stemming works published from 1968 to 2023. The work presents a multidimensional review of studied stemming algorithms i.e., methodology, data source, performance, and evaluation methods. For this study, we have chosen different stemmers, which can be categorized as 1) linguistic knowledge-based, 2) statistical, 3) corpus-based, 4) context-sensitive, and 5) hybrid stemmers. The study shows that linguistic knowledge-based stemming techniques were widely used for highly inflected languages (such as Arabic, Hindi, and Urdu) and have reported higher accuracy than other techniques. We compare and analyze the performance of various state-of-the-art TS approaches, including their issues and challenges, which are summarized as research gaps. This work also analyzes different NLP applications utilizing stemming methods. At the end, we list the future work directions for interested researchers.

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Section: Context-sensitive Approachesmentioning

confidence: 99%