Abstract
This chapter addresses the issue of multilingual corpora alignment, presenting schemes which attempt alignment at sentence, clause, noun phrase and word level. Statistical inductive techniques are coupled with symbolic processing analysing specific language phenomena. Sentence alignment combines statistical techniques with the notion of semantic load of text units. Lexical equivalences are extracted based on morphosyntactic tagging and noun phrase recognition on each side of the parallel corpus. A statistical score then filters the most likely translation candidates of single and multi-word units. Similarly, clause alignment couples surface linguistic analysis with a probabilistic model based on word occurrence and cooccurrence probabilities, and word lengths. The best clause alignment is approximated by feeding all possible alignments into a dynamic programming framework. Word and clause alignment have been tested on English-Greek parallel corpora of different domains, yielding results exploitable in knowledge acquisition applications. Sentence alignment has been tested in several languages and integrated in a computer-aided translation platform maximizing translation reuse and consistency.
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Piperidis, S., Papageorgiou, H., Boutsis, S. (2000). From sentences to words and clauses. In: Véronis, J. (eds) Parallel Text Processing. Text, Speech and Language Technology, vol 13. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-2535-4_6
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DOI: https://doi.org/10.1007/978-94-017-2535-4_6
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