Abstract
Natural language support is a powerful feature that enhances user interaction with query systems. NLP requires dealing with ambiguities. Traditional probabilistic parsers provide a convenient means for disambiguation. However, they incorrigibly return wrong sequences of tokens, they impose hard constraints on the way lexical and syntactic ambiguities can be resolved, and they are limited in the mechanisms they allow for taking context into account. In comparison, model-based parser generators allow for flexible constraint specification and reference resolution, which facilitates the context consideration. In this paper, we explain how the ModelCC model-based parser generator supports statistical language models and arbitrary probability estimators. Then, we present the ModelCC implementation of a natural language parser based on the syntax of most Romance and Germanic languages. This natural language parser can be instantiated for a specific language by connecting it with a thesaurus (for lexical analysis), a linguistic corpus (for syntax-driven disambiguation), and an ontology or semantic database (for semantics-driven disambiguation).
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Quesada, L., Berzal, F., Cubero, JC. (2013). A Model-Based Multilingual Natural Language Parser — Implementing Chomsky’s X-bar Theory in ModelCC. In: Larsen, H.L., Martin-Bautista, M.J., Vila, M.A., Andreasen, T., Christiansen, H. (eds) Flexible Query Answering Systems. FQAS 2013. Lecture Notes in Computer Science(), vol 8132. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40769-7_26
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DOI: https://doi.org/10.1007/978-3-642-40769-7_26
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