Natural Language Engineering


Linguistic knowledge in statistical phrase-based word alignment

A. DE GISPERT a1 and J. B. MARIÑO a1
a1 TALP Research Center, Universitat Politècnica de Catalunya (UPC), Jordi Girona 1-3, Campus Nord D5, 08034 Barcelona, Spain e-mail:,

Article author query
de gispert a   [Google Scholar] 
marino jb   [Google Scholar] 


In this paper, a novel phrase alignment strategy combining linguistic knowledge and cooccurrence measures extracted from bilingual corpora is presented. The algorithm is mainly divided into four steps, namely phrase selection and classification, phrase alignment, one-to-one word alignment and postprocessing. The first stage selects a linguistically-derived set of phrases that convey a unified meaning during translation and are therefore aligned together in parallel texts. These phrases include verb phrases, idiomatic expressions and date expressions. During the second stage, very high precision links between these selected phrases for both languages are produced. The third step performs a statistical word alignment using association measures and link probabilities with the remaining unaligned tokens, and finally the fourth stage takes final decisions on unaligned tokens based on linguistic knowledge. Experiments are reported for an English-Spanish parallel corpus, with a detailed description of the evaluation measure and manual reference used. Results show that phrase cooccurrence measures convey a complementary information to word cooccurrences and a stronger evidence of a correct alignment, successfully introducing linguistic knowledge in a statistical word alignment scheme. Precision, Recall and Alignment Error Rate (AER) results are presented, outperforming state-of-the-art alignment algorithms.

(Published Online December 6 2005)
(Received May 4 2004)
(Revised May 10 2005)