Domain word translation by space-frequency analysis of context length histograms

We report a new statistical feature relating a bilingual word pair in a non-parallel English-Chinese corpus. It is found that the lengths of context segments of a word are closely correlated to that of the translation, even when the corpus is non-parallel, i.e., monolingual texts which are not translations of each other. The context segment length histogram of a word has a characteristic pattern and corresponds to that of its translation. If a word appears most frequently in long segments, its translation is found to be most likely occurring in long segments. One way to match these histograms is to first extract their salient shape characteristics by space-frequency analysis and then match them against each other using dynamic time warping. The results of matching can be used in combination with other statistical features to bootstrap a word or term translation algorithm from non-parallel corpora