Lexical Ambiguity in Arabic Information Retrieval: The Case of Six Web-Based Search Engines
Bibliographic record
Abstract
In recent years, both research and industry have shown an increasing interest in developing reliable information retrieval (IR) systems that can effectively address the growing demands of users worldwide. In spite of the relative success of IR systems in addressing the needs of users and even adapting to their environments, many problems remain unresolved. One main problem is lexical ambiguity which has negative impacts on the performance and reliability of IR systems. To date, lexical ambiguity has been one of the most frequently reported problems in the Arabic IR systems despite the development of different word sense disambiguation (WSD) techniques. This is largely attributed to the limitations of such techniques in addressing the issue of linguistic peculiarities. Hence, this study addresses these limitations by exploring the reasons for lexical ambiguity in IR applications in Arabic as one step towards reliable and practical solutions. For this purpose, the performances of six search engines Google, Bing, Baidu, Yahoo, Yandex, and Ask are evaluated. Results indicate that lexical ambiguities in Arabic IR applications are mainly due to the unique morphological and orthographic system of the Arabic language, in addition to its diglossia and the multiple colloquial dialects where sometimes mutual intelligibility is not achieved. For better disambiguation and IR performances in Arabic, this study proposes that clustering models based on supervised machine learning theory should be trained to address the morphological diversity of Arabic and its unique orthographic system. Search engines should also be adapted to the geographic location of the users in order to address the issue of vernacular dialects of Arabic. They should also be trained to automatically identify the different dialects. Finally, search engines should consider all varieties of Arabic and be able to interpret the queries regardless of the particular language adopted by the user.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".