This paper presents a currently bilingual but potentially multilingual FrameNet-based grammar library implemented in Grammatical Framework. The contribution of this paper is two-fold. First, it offers a methodological approach to automatically generate the grammar based on semantico-syntactic valence patterns extracted from FrameNet-annotated corpora. Second, it provides a proof of concept for two use cases illustrating how the acquired multilingual grammar can be exploited in different CNL applications in the domains of arts and tourism.
The grounding of NLG using the frame semantics theory has been addressed in the work on text-to-scene generation @cite_13 and in the work on text generation for navigational tasks @cite_7 . In that research, the content of frames is utilized through alignment between the frame-semantic structure and the domain-semantic representation. Discourse is supported by applying aggregation and pronominalization techniques. In the CH use case, we also show how an application which utilizes the FN-based grammar can become more discourse-oriented; something that is necessary in actual NLG applications and that has been demonstrated for the CH domain in GF before @cite_16 . In our current approach, the semantic representation of the domain and the linguistic structures of the grammar are based on FN-annotated data.
@cite_16 @cite_13 @cite_7
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This paper introduces Vignette Semantics, a lexical semantic theory based on Frame Semantics that represents conceptual and graphical relations. We also describe a lexical resource that implements this theory, VigNet, and its application in text-to-scene generation. Route directions are natural language (NL) statements that specify, for a given navigational task and an automatically computed route representation, a sequence of actions to be followed by the user to reach his or her goal. A corpus-based approach to generate route directions involves (i) the selection of elements along the route that need to be mentioned, and (ii) the induction of a mapping from route elements to linguistic structures that can be used as a basis for NL generation. This paper presents an Expectation-Maximization (EM) based algorithm that aligns geographical route representations with semantically annotated NL directions, as a basis for the above tasks. We formulate one basic and two extended models, the latter capturing special properties of the route direction task. Although our current data set is small, both extended models achieve better results than the simple model and a random baseline. The best results are achieved by a combination of both extensions, which outperform the random baseline and the simple model by more than an order of magnitude.