tailieunhanh - Báo cáo khoa học: "Machine-learned contexts for linguistic operations in German sentence realization"

This set of candidate surface strings, represented as a word lattice, is then rescored by a wordbigram language model, to produce the bestranked output sentence. FERGUS (Bangalore and Rambow, 2000), on the other hand, employs a model of syntactic structure during sentence realization. In simple terms, it adds a tree-based stochastic model to the approach taken by the Nitrogen system. This tree-based model chooses a best-ranked XTAG representation for a given dependency structure. Possible linearizations of the XTAG representation are generated and then evaluated by a language model to pick the best possible linearization, as in Nitrogen. . | Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics ACL Philadelphia July 2002 pp. 25-32. Machine-learned contexts for linguistic operations in German sentence realization Michael GAMON Eric RINGGER Simon CORSTON-OLIVER Robert MOORE Microsoft Research Microsoft Corporation Redmond wA 98052 mgamon ringger simonco bobmoore @ Abstract We show that it is possible to learn the contexts for linguistic operations which map a semantic representation to a surface syntactic tree in sentence realization with high accuracy. We cast the problem of learning the contexts for the linguistic operations as classification tasks and apply straightforward machine learning techniques such as decision tree learning. The training data consist of linguistic features extracted from syntactic and semantic representations produced by a linguistic analysis system. The target features are extracted from links to surface syntax trees. Our evidence consists of four examples from the German sentence realization system code-named Amalgam case assignment assignment of verb position features extraposition and syntactic aggregation 1 Introduction The last stage of natural language generation sentence realization creates the surface string from an abstract typically semantic representation. This mapping from abstract representation to surface string can be direct or it can employ intermediate syntactic representations which significantly constrain the output. Furthermore the mapping can be performed purely by rules by application of statistical models or by a combination of both techniques. Among the systems that use statistical or machine learned techniques in sentence realization there are various degrees of intermediate syntactic structure. Nitrogen Langkilde and Knight 1998a 1998b produces a large set of alternative surface realizations of an input structure which can vary in abstractness . This set of candidate surface strings represented as a word

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