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Should MT Systems Be Used as Black Boxes in CLIR?

descriptionPublicationkeyboard_double_arrow_right Part of book or chapter of book , Conference object 01 Jan 2011 Ireland Publisher:Springer Berlin HeidelbergPublicly fundedFunded by:SFI | CSET CNGL: Next Generatio...

Authors: Magdy, Walid; Jones, Gareth J.F.;

doi: 10.1007/978-3-642-20161-5_70

Should MT Systems Be Used as Black Boxes in CLIR?

- Summary
- Subjects
- References
  (1)
- Metrics

Abstract

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.

Country

Ireland

Related Organizations

Subjects by Vocabulary

Microsoft Academic Graph classification: Speedup Information retrieval Stop words Machine translation Computer science Process (engineering) business.industry Transfer-based machine translation Translation (geometry) computer.software_genre Artificial intelligence business computer Cross-language information retrieval Natural language processing Sentence

Keywords

Information retrieval

Koehn P., H. Hoang, A. Birch, C. Callison-Burch, M. Federico, N. Bertoldi, B. Cowan, W. Shen, C. Moran, R. Zens, C. Dyer, O. Bojar, A. Constantin, E. Herbst, Moses: Open Source Toolkit for Statistical Machine Translation. In ACL07. (2007) Magdy W. and G. J. F. Jones. PRES: a score metric for evaluating recall-oriented information retrieval applications. In SIGIR 2010. (2010) Magdy W. and G. J. F. Jones. Applying the KISS Principle for the CLEF-IP 2010 Prior Art Candidate Patent Search Task. In CLEF2010. (2010) Oard, D. W. and Diekema, A. R. Cross-Language Information Retrieval. In: WILLIAMS, M. (ed.) ARIST. (1998) Piroi F. CLEF-IP 2010: Retrieval Experiments in the Intellectual Property Domain. In CLEF 2010. (2010) Strohman T., D. Metzler, H. Turtle, and W. B. Croft. Indri: A language model-based search engine for complex queries. In ICIA, (2004) Stroppa, N. and A. Way. 2006. MaTrEx: DCU Machine Translation System for IWSLT 2006. In IWSLT. (2006)

Impact byBIP!

	citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	9
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Average
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Average
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Top 10%

	citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	9
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Average
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Average
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Top 10%