Sentence Alignment by Means of Cross-Language Information Retrieval [chapter]

Marta R., Rafael E.
2011 Speech and Language Technologies  
different phases: analysis, transfer and generation. Therefore, a rule-based system requires: syntax analysis, semantic analysis, syntax generation and semantic generation. Statistical Machine Translation (SMT), a corpus-based approach, is a more complicated form of word translation, where statistical weights are used to decide the most likely translation of a word. Modern SMT systems are phrase-based rather than word-based, and assemble translations using the overlap in phrases. Organization
more » ... the chapter The rest of this chapter is structured as follows. Next section describes several sentence alignment approaches. Section 4 reports the motivation of our CLIR approach. Section 5 describes in detail how our sentence alignment system works. Section 6 describes the two machine translation approaches that are used and compared in this chapter: rule-based and statistically-based. Next, experimental framework and the proposed methodology are illustrated by performing cross-language text matching at the sentence level on a tetra-lingual document collection. Also, within this section, the performance quality of the implemented systems is compared, showing that in this application the statistical system provides better results than the rule-based system. Section 8 reports the translation quality of both translation systems and reports the correlation among translation quality and cross-language sentence matching quality. Finally, in section 9, most relevant conclusions derived from the experimental results are presented. Related work Sentence alignment has been approached from different perspectives. In the following subsections we briefly describe some well-known methods. • Gale & Church (1993) proposed a sentence alginer provided a probability score for each sentence pair based on sentence-length (number of characters). Their method use dynamic programming to find maximum likelihood alignment.
doi:10.5772/16243 fatcat:uslm3w5qcrawhcngmtmksix3ha