An Automatic Maximum Word Alignment of Parallel Corpus for ESL Learners

In this paper, we propose an approach that automatically aligns words extracted from paired sentences in English- Korean parallel corpus. Word-to-word mappings from aligned sentences are easily accomplished if a bilingual lexicon is provided. However, newly coined words cannot be aligned automatically since they are not registered in the lexicon. For automated alignment of newly identified words, we introduce new heuristics to align maximal number of words, using structural transfer patterns between the two languages. Provided maximum word alignments together with paired sentences in a parallel corpus, Korean learners of English can benefit from recognizing distinctions on sentential structures as well as vocabularies between the two languages.