Statistical Word-Level Translation Model for Comparable Corpora

摘要

In this paper, we present a model of statistical word-level mapping for comparable corpora. The approach is based on the assumption that if two terms have close distributional profiles, their corresponding translations' distributional profiles should be close in a comparable corpus. The proposed model is described. A preliminary investigation on intralanguage comparable corpora is laid out. The preliminary results are >92% accurate suggesting the feasibility of the model. The model needs to undergo some improvements and should be tested cross linguistically before assessing its significance.