Dictionary-Based Cross-Language Information Retrieval: Learning Experiences from CLEF 2000–2002

Hedlund, Turid; Airio, Eija; Keskustalo, Heikki; Lehtokangas, Raija; Pirkola, Ari; Järvelin, Kalervo

doi:10.1023/b:inrt.0000009442.34054.55

Cited by 39 publications

(23 citation statements)

References 21 publications

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“…In Finnish however, these irregularities are more common, thus rendering the conflation of various word forms into the same stem more problematic. Thus, in order to index Finnish documents, some authors suggest using a morphological analyzer (based on a dictionary) [4].…”

Section: Stopword Lists and Stemming Proceduresmentioning

confidence: 99%

Data Fusion for Effective European Monolingual Information Retrieval

Savoy

2005

Multilingual Information Access for Text, Speech and Images

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Abstract. For our fourth participation in the CLEF evaluation campaigns, our first objective was to propose an effective and general stopword list and a light stemming procedure for the Portuguese language. Our second objective was to obtain a better picture of the relative merit of various search engines when processing documents in the Finnish and Russian languages. Finally, based on the Z-score method we suggested a data fusion strategy intended to improve monolingual searches in various European languages.

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Section: Stopword Lists and Stemming Proceduresmentioning

confidence: 99%

Data Fusion for Effective European Monolingual Information Retrieval

Savoy

2005

Multilingual Information Access for Text, Speech and Images

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“…La première consiste à traduire les documents au complet dans toutes les langues, alors que la deuxième suppose la traduction des requêtes dans la langue des documents à repérer (Oard et Ertune, 2002). Ces deux approches font généralement appel à trois types de ressources linguistiques pour la traduction : les dictionnaires bilingues ou multilingues (Hedlund et al, 2004), les systèmes de traduction automatique (TA) (Chen et Gey, 2004), et les corpus parallèles ou comparables (Braschler et Schâuble, 2000). Ces diffé-rentes ressources linguistiques ont chacune démontré leurs forces et leurs faiblesses.…”

Section: Sur La Lancée Des Campagnes D'évaluation Duunclassified

La recherche d’information multilingue

Ménard

2015

documentation

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“…Machine translation and dictionary-based technique. In this technique, queries are translated using a dictionary into a language in which a document may be found (Hull and Grefenstette 1996;Oard 1998;Pirkola et al 2001;Hedlund et al 2004). An example of this approach is a multilingual search engine, called TITAN (Hayashi et al 1997), where an electronic bilingual (English-Japanese) dictionary helped Japanese users to search the web using their own language.…”

Section: Cross Language Information Retrievalmentioning

confidence: 99%

Towards enhancing retrieval effectiveness of search engines for diacritisized Arabic documents

Hammo

2008

Inf Retrieval

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The majority of Arabic text available on the web is written without short vowels (diacritics). Diacritics are commonly used in religious scripts such as the holy Quran (the book of Islam), Al-Hadith (the teachings of Prophet Mohammad (PBUH)), children's literature, and in some words where ambiguity of articulation might arise. Internet Arabic users might lose credible sources of Arabic text to be retrieved if they could not match the correct diacritical marks attached to the words in the collection. However, typing the diacritical marks is very annoying and time consuming. The other way around, is to ignore these marks and fall into the problem of ambiguity. Previous work suggested pre-processing of Arabic text to remove these diacritical marks before indexing. Consequently, there are noticeable discrepancies when searching the web for Arabic text using international search engines such as Google and yahoo. In this article, we propose a framework to enhance the retrieval effectiveness of search engines to search for diacritic and diacritic-less Arabic text through query expansion techniques. We used a rule-based stemmer and a semantic relational database compiled in an experimental thesaurus to do the expansion. We tested our approach on the scripts of the Quran. We found that query expansion for searching Arabic text is promising and it is likely that the efficiency can be further improved by advanced natural language processing tools.

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Dictionary-Based Cross-Language Information Retrieval: Learning Experiences from CLEF 2000–2002

Cited by 39 publications

References 21 publications

Data Fusion for Effective European Monolingual Information Retrieval

Data Fusion for Effective European Monolingual Information Retrieval

La recherche d’information multilingue

Towards enhancing retrieval effectiveness of search engines for diacritisized Arabic documents

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