Predictive power of semantic information in the use context of multiword terms for their structural disambiguation

Introduction: The structural disambiguation of English multiword terms (MWT) of three or more constituents (e.g., coastal sediment transport), often known as bracketing, involves the grouping of the dependent components so that the MWT is reduced to its basic form of modifier+head, as in coastal [sediment transport], which is a right-bracketed ternary compound. This work presents a study that explored whether the bracketing of a ternary compound, when used as an argument in a sentence, can be predicted from the semantic information encoded in that sentence. Methodology: A set of 1.694 sentences were analyzed semantically and annotated with the lexical domain of the verbs, the semantic role and category of the arguments, and the semantic relation between the arguments. These semantic variables were then analyzed statistically to determine whether they are able to predict the bracketing of a ternary compound. Results: A random forest model, with the lexical domain of the verb, and the semantic role and category of the MWT, was able to predict the bracketing of the ternary compounds used as arguments in a sample of 380 MWTs (100% F1‑score). A decision tree, with solely the semantic relation of the MWT to another argument in the same sentence, was also able to predict the bracketing of the ternary compounds in the sample (94,12% F1‑score). Discussion: Only a subset of three variables was necessary for bracketing prediction with an error free performance, whereas previous research employed a minimum of 12 variables. Conclusion: The semantic information in a sentence contributed substantially to compound parsing. This suggests a novel research direction in the integration of semantic variables into syntactic parsers and machine-translation applications.