Empirical studies in strategies for Arabic retrieval

Xu, Jinxi; Fraser, Alexander; Weischedel, Ralph

doi:10.1145/564376.564424

Cited by 72 publications

(56 citation statements)

References 22 publications

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“…Previous work on word sense disambiguation suggests that translation usage can provide a useful basis for identifying terms with similar meaning (Resnik and Yarowsky, 2000;Xu et al, 2002). The key idea is that if term f in language F can translate to a term e i in language E, which can further back-translate to some term f j in language F , then f j might be a synonym of f .…”

Section: Using Synsets To Represent Meaningmentioning

confidence: 99%

Matching meaning for cross-language information retrieval

Wang

Oard

2012

Information Processing & Management

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This article describes a framework for cross-language information retrieval that efficiently leverages statistical estimation of translation probabilities. The framework provides a unified perspective into which some earlier work on techniques for cross-language information retrieval based on translation probabilities can be cast. Modeling synonymy and filtering translation probabilities using bidirectional evidence are shown to yield a balance between retrieval effectiveness and query-time (or indexing-time) efficiency that seems well suited large-scale applications. Evaluations with six test collections show consistent improvements over strong baselines.

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Section: Using Synsets To Represent Meaningmentioning

confidence: 99%

Matching meaning for cross-language information retrieval

Wang

Oard

2012

Information Processing & Management

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“…They also showed that a crude method like trigram can be employed in the first stage of that process with little loss of performance. They have also applied their trigram approach to other languages such as Spanish [20]. The main assumption in this approach is that words that belong to the same equivalence class (i.e.…”

Section: Related Researchmentioning

confidence: 99%

Corpus-Based Arabic Stemming Using N-Grams

Zitouni

Damankesh

Barakati

et al. 2010

Information Retrieval Technology

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Abstract. In languages with high word inflation such as Arabic, stemming improves text retrieval performance by reducing words variants. We propose a change in the corpus-based stemming approach proposed by Xu and Croft for English and Spanish languages in order to stem Arabic words. We generate the conflation classes by clustering 3-gram representations of the words found in only 10% of the data in the first stage. In the second stage, these clusters are refined using different similarity measures and thresholds. We conducted retrieval experiments using row data, Light-10 stemmer and 8 different variations of the similarity measures and thresholds and compared the results. The experiments show that 3-gram stemming using the dice distance for clustering and the EM similarity measure for refinement performs better than using no stemming; but slightly worse than Light-10 stemmer. Our method potentially could outperform Light-10 stemmer if more text is sampled in the first stage.

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“…We found out that in order to build a good IR system for Arabic texts it is very important to take in considerations some crucial aspects: (ii) Stop words: Many researches used the stop words list that was published by Yaser Al-Onaizan in his web site 5 ; (unfortunately, this URL is not available anymore;) (iii) Query expansion: All the participants, used a blind feedback expansion. Lately, [29] carried out a study which showed that using a thesaurus for query expansion gives better results than the blind feedback expansion. The authors argue that this technique performed better because synonyms are widely used in Arabic texts because variety in expression is appreciated as a good writing style.…”

Section: Retrieving Passages In Arabicmentioning

confidence: 99%

Adapting the JIRS Passage Retrieval System to the Arabic Language

Benajiba

Rosso

Soriano

2007

Lecture Notes in Computer Science

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Abstract. The need of having a Passage Retrieval (PR) system for Arabic texts is due essentially to our aim to build an Arabic Question Answering (QA) system in our research team. We have chosen working on the PR system to be our first step to pursue our aim because being the core component and its quality will affect directly the performance of the QA system. JAVA Information Retrieval System (JIRS) is a PR QA-oriented system, multi-platform, open source and free to use. JIRS uses an n-gram model and it is language-independent. It separates language configuration files to make easier its adaptation to any language. In this paper, we report the different challenges when adapting the JIRS to the Arabic language.In order to evaluate JIRS on Arabic, we had to develop an Arabic test-bed using the multilingual CLEF QA one as guideline. We also report the results obtained in our experiments where we retrieved Arabic passages with JIRS first without any text preprocessing and second performing a prior light-stemming on the documents of the test-bed. The preliminary results show that it is possible to obtain a first Arabic passage retrieval system adapting JIRS on pre-processed text with a light-stemmer.

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Empirical studies in strategies for Arabic retrieval

Cited by 72 publications

References 22 publications

Matching meaning for cross-language information retrieval

Matching meaning for cross-language information retrieval

Corpus-Based Arabic Stemming Using N-Grams

Adapting the JIRS Passage Retrieval System to the Arabic Language

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