2012/12/05 by Miquel Esplà-Gomis, Esplà-Gomis, Miquel, Felipe Sánchez-Martínez +3
Computer Science · #Computation and Language (cs.CL) #FOS: Computer and information sciences #I.2.7 #Natural Language Processing Techniques #Text Readability and Simplification #Topic Modeling
paper · pdf · doi:10.48550/arxiv.1212.1192
openalex publication_date 2012/12/05 · openalex created_date 2024/04/11 · openalex updated_date 2026/07/28
In this paper we present a new and simple language-independent method for word-alignment based on the use of external sources of bilingual information such as machine translation systems. We show that the few parameters of the aligner can be trained on a very small corpus, which leads to results comparable to those obtained by the state-of-the-art tool GIZA++ in terms of precision. Regarding other metrics, such as alignment error rate or F-measure, the parametric aligner, when trained on a very small gold-standard (450 pairs of sentences), provides results comparable to those produced by GIZA++ when trained on an in-domain corpus of around 10,000 pairs of sentences. Furthermore, the results obtained indicate that the training is domain-independent, which enables the use of the trained aligner 'on the fly' on any new pair of sentences.