Abstract
We propose a content-based method of mining bilingual parallel documents from websites that are not necessarily structurally related to each other. There are two existing approaches for automatically mining parallel documents from the web. Structure based methods work only for parallel websites and most of content based methods are either requires large scale computational facilities, network bandwidth or not applicable to heterogeneous web. We propose a novel content based method using cross lingual information retrieval (CLIR) with query feedback and verification and supplemented with structural information, to mine parallel resources from the entire web using search engine APIs. The method goes beyond structural information to find parallel documents from non-parallel websites. We obtained a very high mining precision and extracted parallel sentences improved SMT performance, with higher BLEU score, is comparable to that obtained with high quality manually translated parallel sentences illustrating the excellent quality of the mined parallel materiel
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Notes
- 1.
We knew the web was big \(\ldots \) on the Official Google Blog. http://googleblog.blogspot.com/2008/07/we-knew-web-was-big.html.
- 2.
Source: http://cn.reuters.com/article/CNTechNews/idCNCHINA-3233720101027 on May 10, 2011.
- 3.
- 4.
LDC Catalog Number: LDC2002L27.
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This project is partially funded by a subcontract from BBN, under the DARPA GALE project.
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Shi, S., Fung, P. (2013). Mining Parallel Documents Using Low Bandwidth and High Precision CLIR from the Heterogeneous Web. In: Sharoff, S., Rapp, R., Zweigenbaum, P., Fung, P. (eds) Building and Using Comparable Corpora. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20128-8_2
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