Login (DCU Staff Only)
Login (DCU Staff Only)

DORAS | DCU Research Repository

Explore open access research and scholarly works from DCU

Advanced Search

Should MT systems be used as black boxes in CLIR?

Magdy, Walid and Jones, Gareth J.F. orcid logoORCID: 0000-0003-2923-8365 (2011) Should MT systems be used as black boxes in CLIR? In: The 33rd European Conference on Information Retrieval (ECIR 2011), 18-21 April 2011, Dublin, Ireland.

The translation stage in cross language information retrieval (CLIR) acts as the main enabling stage to cross the language barrier between documents and queries. In recent years machine translation (MT) systems have become the dominant approach to translation in CLIR. However, unlike information retrieval (IR), MT focuses on the morphological and syntactical quality of the sentence. This requires large training resources and high computational power for training and translation. We present a novel technique for MT designed specifically for CLIR. In this method IR text pre-processing in the form of stop word removal and stemming are applied to the MT training corpus prior to the training phase. Applying this pre-processing step is found to significantly speed up the translation process without affecting the retrieval quality.
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Uncontrolled Keywords:cross language information retrieval; CLIR; machine translation; MT; language corpus
Subjects:Computer Science > Information retrieval
DCU Faculties and Centres:Research Institutes and Centres > Centre for Next Generation Localisation (CNGL)
DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
ID Code:16395
Deposited On:29 Jun 2011 14:02 by Shane Harper . Last Modified 25 Oct 2018 10:19

Full text available as:

[thumbnail of Should_MT_Systems_be_Used_as_Black_Boxes_in_CLIR.pdf]
PDF - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader


Downloads per month over past year

Archive Staff Only: edit this record