Abstract
In this paper we present a graph-based approach to question answering. The method assumes a graph representation of question sentences and text sentences. Question answering rules are automatically learnt from a training corpus of questions and answer sentences with the answer annotated. The method is independent from the graph representation formalism chosen. A particular example is presented that uses a specific graph representation of the logical contents of sentences.
Cite
CITATION STYLE
Mollá, D. (2020). Learning of graph-based question answering rules. In Proceedings of TextGraphs: The 1st Workshop on Graph-Based Methods for Natural Language Processing (pp. 37–44). Association for Computational Linguistics. https://doi.org/10.3115/1654758.1654768
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