Abstract:

Automatic extraction of bilingual lexicons from parallel corpora has been recently exploited to overcome the knowledge acquisition bottleneck in a number of research areas in natural language processing, such as machine translation (MT) and cross-language information retrieval. In this paper the author presents a method for automatic extraction of bilingual lexicons from a parallel Arabic-English corpus annotated with part-of-speech tags for potential use in a full-scale MT system. The extraction method, which does not make use of an initial bilingual dictionary, is based on the statistical technique of co-occurrence frequency in the parallel corpus. In addition, dependency relations for some parallel syntactic constructions are made use of to automatically extract head-dependent translation pairs which are then filtered to obtain one-word translation seeds.  These seeds are used as anchor points to resegment the parallel corpus in order to bootstrap the lexicon extraction process. Experimental results show that the accuracy of the extracted lexicons was improved after applying the bootstrapping techniques.