BiFrameNet: Bilingual Frame Semantics Resource Construction by Cross-lingual Induction

BiFrameNet: Bilingual Frame Semantics Resource Construction by Cross-lingual Induction Pascale Fung and Benfeng Chen Human Language Technology Center, University of Science & Technology (HKUST), Clear Water Bay, Hong Kong {pascale,bfchen}@ee.ust.hk Abstract 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. 1. Introduction The merits of translation at the word level or the concept level have long been a cause for debate among linguists. Some linguists suggest that the two languages of a bilingual speaker share a common semantic system (Illes and Francis 1999; Ikeda 1998) and hence translation is carried out at the concept level. Meanwhile, there has been a gradual convergence of statistical and transfer approaches in machine translation recently (Wu 2003). Statistical MT systems are based on a stochastic mapping between lexical items, assuming the underlying semantic transfer is hidden. Transfer systems use explicit lexical, syntactic and semantic transfer rules. Consequently, cognitive scientists and computational linguists alike have been interested in the study of semantic mapping between languages (Ploux and Ji, 2003, Dorr et al., 2002, Ngai et al., 2002, Boas 2002, Palmer and Wu, 1995). We propose to automatically construct a bilingual lexical semantic network with word sense and semantic role mapping between English and Chinese, simulating the “concept lexicon”, suggested by cognitive scientists, of a bilingual person. Figure 1. BiFrameNet lexicon and example sentence induction The linguists-defined ontologies–-FrameNet (Baker et al., 1998), HowNet (Dong and Dong, 2000), and bilingual dictionaries are the basis for the induction of the mapping. We automatically estimate the semantic transfer likelihoods between English FrameNet lexical entries and the Chinese word senses in HowNet, and align those frames and lexical pairs with high likelihood values. In addition, we propose to induce Chinese example sentences automatically to match English annotated sentences provided in the FrameNet. The BiFrameNet thus induced provides an additional resource for machine-aided or machine translation systems. It can also serve as a reference to be compared to cognitive studies of the translation process. : le x ic a l e n try in F ra …

Content truncated to 500 words