Using N-Gram Models to Combine Query Translations in Cross-Language Question Answering

Aceves-Pérez, Rita M.; Villaseñor-Pineda, Luis; Montes-y-Gómez, Manuel

doi:10.1007/11671299_47

Cited by 3 publications

(3 citation statements)

References 3 publications

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“…Many are the systems participating in different tasks of CLEF 2005 [24], [12] and 2006 [6] [25], [10] are JIRS-based. This shows that JIRS can be also employed in other NLP tasks than just QA [1]. JIRS proved to be efficient for the Spanish, Italian, French and English languages.…”

Section: Introductionmentioning

confidence: 84%

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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Section: Introductionmentioning

confidence: 84%

Adapting the JIRS Passage Retrieval System to the Arabic Language

Benajiba

Rosso

Soriano

2007

Lecture Notes in Computer Science

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“…[42] proposes a candidate question translation obtained by means of different on-line machine translators and also implements some heuristics in order to correct and combine the on-line translations to achieve an acceptable one. Finally, the three methods exposed in [1,2] try to improve the quality of the translation of the query. The first one focuses on selecting the most fluent translation from a given set; the second combines the passages recovered by several question translations; and the third constructs a reformulation is a new question by merging word sequences from different translations.…”

Section: Related Work On Qa Task In CL Environmentsmentioning

confidence: 99%

“…Combining Multiple Query Translation. These systems process different translations in order to obtain an optimal translation [42,30,27,1,2].…”

Section: Related Work On Qa Task In CL Environmentsmentioning

confidence: 99%