FrameNet Resource Grammar Library for GF
This work addresses the difficulty for GF developers in porting less restricted controlled natural languages across over 20 languages by reducing the need for deep linguistic and GF expertise, though it appears incremental as it builds on existing GF infrastructure.
The paper tackles the challenge of integrating FrameNet frame semantics into the Grammatical Framework (GF) to simplify multilingual application grammar development, particularly for verbs, by proposing an extension to the Resource Grammar Library that allows semantic-level clause definitions and demonstrates this with the MOLTO Phrasebook reengineering.
In this paper we present an ongoing research investigating the possibility and potential of integrating frame semantics, particularly FrameNet, in the Grammatical Framework (GF) application grammar development. An important component of GF is its Resource Grammar Library (RGL) that encapsulates the low-level linguistic knowledge about morphology and syntax of currently more than 20 languages facilitating rapid development of multilingual applications. In the ideal case, porting a GF application grammar to a new language would only require introducing the domain lexicon - translation equivalents that are interlinked via common abstract terms. While it is possible for a highly restricted CNL, developing and porting a less restricted CNL requires above average linguistic knowledge about the particular language, and above average GF experience. Specifying a lexicon is mostly straightforward in the case of nouns (incl. multi-word units), however, verbs are the most complex category (in terms of both inflectional paradigms and argument structure), and adding them to a GF application grammar is not a straightforward task. In this paper we are focusing on verbs, investigating the possibility of creating a multilingual FrameNet-based GF library. We propose an extension to the current RGL, allowing GF application developers to define clauses on the semantic level, thus leaving the language-specific syntactic mapping to this extension. We demonstrate our approach by reengineering the MOLTO Phrasebook application grammar.