We present a novel automatic approach to constructing a bilingual semantic network-the BiFrameNet, to enhance statistical and transfer-based machine translation systems. BiFrameNet is a frame semantic representation, and contains semantic structure transfers between English and Chinese. The English FrameNet and the Chinese HowNet provide us with two different views of the semantic distribution of lexicon by linguists. We propose to induce the mapping between the English lexical entries in FrameNet to Chinese word senses in HowNet, furnishing a bilingual semantic lexicon which simulates the "concept lexicon" supposedly used by human translators, and which can thus be beneficial to machine translation systems. BiFrameNet also contains bilingual example sentences that have the same semantic roles. We automatically induce Chinese example sentences and their semantic roles, based on semantic structure alignment from the first stage of our work, as well as shallow syntactic structure. In addition to its utility for machine-aided and machine translations, our work is also related to the spatial models proposed by cognitive scientists in the framework of artifactual simulations of the translation process.
Similarly to REF we automatically induce semantic role annotations for a target language.
BiFrameNet: Bilingual Frame Semantics Resource Construction By Cross-Lingual Induction
International Conference On Computational Linguistics
Computer Science