Abstract
Ungrammatical sentences present a challenge in a number of Natural Language Processing tasks, including those used in automatic Question Answering. In this paper, we introduce an algorithm that identifies the most likely decomposition of a (possibly ungrammatical) sentence into its semantic roles. The algorithm makes use of a chart parser - using a “tight” hybrid syntactic-semantic context-free grammar - that identifies whether each substring may play the role of either a main or a subordinate clause (like a declarative clause), or a semantic role like subject, predicate or complements. Then an Integer Programming Problem is solved in order to find a coverage of maximum likelihood. At this stage, the model tries to partition the sentence in substrings in such a way that: (a) each substring is assigned a clause (main or a secondary clause) and a semantic role; a measure of the overall likelihood is maximized. The validity of this approach has been assessed on a testset obtained by randomly perturbing a set of grammatical sentences of various nature.
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Acknowledgment
This work was partly supported by the Ministero dell’Istruzione, dell’Università e della Ricerca (MIUR) of Italy. This support is gratefully acknowledged.
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Ghiani, G., Guerrieri, A., Manni, A. (2019). Extracting Semantic Roles from Ungrammatical Sentences. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Systems and Applications. IntelliSys 2018. Advances in Intelligent Systems and Computing, vol 869. Springer, Cham. https://doi.org/10.1007/978-3-030-01057-7_91
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DOI: https://doi.org/10.1007/978-3-030-01057-7_91
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