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Thesis

French

ID: <

10670/1.1d22o7

>

Where these data come from
Dictionary of verbal diets in mandarin

Abstract

This work fits into the GenDR project, a multilingual deep realizer which models the semantics-syntax interface for natural language generation (NLG). In NLG, lexical resources are essential to transform non-linguistic data into natural language. To a certain extent, the lexical resources used determine the accuracy and flexibility of the sentences generated by a realizer. Due to the unpredictability of verbs’ syntactic behaviour and the central role that verbs play in an utterance, a lexical resource which describes the government patterns of verbs is key to generating the most precise and natural text possible. We aim to create a dictionary of verbs’ government patterns in Mandarin. This kind of lexical resource is still missing for NLG in Mandarin. Based on the Mandarin VerbNet database, we used Python to extract information about adpositions and to create our dictionary. This is a dynamic dictionary whose content can be parameterized according to the user’s needs.

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