Computer Science – Computation and Language
Scientific paper
1997-06-24
Proceedings of the 2nd Conference on Empirical Methods in Natural Language Processing (EMNLP'97), Providence, RI, 1997.
Computer Science
Computation and Language
12 pages; uses natbib.sty, here.sty
Scientific paper
Automatic segmentation of text into minimal content-bearing units is an unsolved problem even for languages like English. Spaces between words offer an easy first approximation, but this approximation is not good enough for machine translation (MT), where many word sequences are not translated word-for-word. This paper presents an efficient automatic method for discovering sequences of words that are translated as a unit. The method proceeds by comparing pairs of statistical translation models induced from parallel texts in two languages. It can discover hundreds of non-compositional compounds on each iteration, and constructs longer compounds out of shorter ones. Objective evaluation on a simple machine translation task has shown the method's potential to improve the quality of MT output. The method makes few assumptions about the data, so it can be applied to parallel data other than parallel texts, such as word spellings and pronunciations.
No associations
LandOfFree
Automatic Discovery of Non-Compositional Compounds in Parallel Data does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Automatic Discovery of Non-Compositional Compounds in Parallel Data, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Automatic Discovery of Non-Compositional Compounds in Parallel Data will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-70060