Enhancing Hybrid Indexing for Arabic Information Retrieval

Guirat, Souheila Ben; Bounhas, Ibrahim; Slimani, Yahya

doi:10.1007/978-3-030-00840-6_27

Cited by 4 publications

(12 citation statements)

References 16 publications

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“…For this goal, they used the LDC's test collection. In Ben Guirat et al (2018), authors combined 3 subsystems using different types of index. The proposed techniques were based on Pseudo Relevance Feedback (PRF) and two data fusionbased approaches namely intelligent sampling and random sampling.…”

Section: Combining Approachesmentioning

confidence: 99%

“…Despite the successful results obtained by such approaches for Chinese and Japanese IR in Kummer et al (2005), Shi et al (2007), their application for Arabic is so far under-investigated especially in data fusion i.e. only in Ben Guirat et al (2018).…”

Section: Combining Approachesmentioning

confidence: 99%

“…Nonetheless, most comparative works (1) did not include Ghwanmeh stemmer which led to good performance; and, (2) did not use different corpora, especially for classical Arabic. As far as combining is concerned, previous works (Ben Guirat et al, 2016Guirat et al, , 2018Guirat et al, , 2019 used only three indexing units, thus ignoring lemmas. What is more, the most recent and popular NLP tools were not used.…”

Section: Proposed Approachmentioning

confidence: 99%

“…For this goal, they used the LDC's test collection. In Ben Guirat et al. (2018), authors combined 3 subsystems using different types of index.…”

Section: Related Workmentioning

confidence: 99%

“…(2007), their application for Arabic is so far under-investigated especially in data fusion i.e. only in Ben Guirat et al. (2018).…”

Section: Related Workmentioning

confidence: 99%

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Meta-search based approach for Arabic information retrieval

Guirat

Bounhas

Slimani

2022

OIR

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PurposeThe semantic relations between Arabic word representations were recognized and widely studied in theoretical studies in linguistics many centuries ago. Nonetheless, most of the previous research in automatic information retrieval (IR) focused on stem or root-based indexing, while lemmas and patterns are under-exploited. However, the authors believe that each of the four morphological levels encapsulates a part of the meaning of words. That is, the purpose is to aggregate these levels using more sophisticated approaches to reach the optimal combination which enhances IR.Design/methodology/approachThe authors first compare the state-of-the art Arabic natural language processing (NLP) tools in IR. This allows to select the most accurate tool in each representation level i.e. developing four basic IR systems. Then, the authors compare two rank aggregation approaches which combine the results of these systems. The first approach is based on linear combination, while the second exploits classification-based meta-search.FindingsCombining different word representation levels, consistently and significantly enhances IR results. The proposed classification-based approach outperforms linear combination and all the basic systems.Research limitations/implicationsThe work stands by a standard experimental comparative study which assesses several NLP tools and combining approaches on different test collections and IR models. Thus, it may be helpful for future research works to choose the most suitable tools and develop more sophisticated methods for handling the complexity of Arabic language.Originality/valueThe originality of the idea is to consider that the richness of Arabic is an exploitable characteristic and no more a challenging limit. Thus, the authors combine 4 different morphological levels for the first time in Arabic IR. This approach widely overtook previous research results.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-11-2020-0515

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Section: Combining Approachesmentioning

confidence: 99%