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Getting More Out Of Syntax with PropS

About

Semantic NLP applications often rely on dependency trees to recognize major elements of the proposition structure of sentences. Yet, while much semantic structure is indeed expressed by syntax, many phenomena are not easily read out of dependency trees, often leading to further ad-hoc heuristic post-processing or to information loss. To directly address the needs of semantic applications, we present PropS -- an output representation designed to explicitly and uniformly express much of the proposition structure which is implied from syntax, and an associated tool for extracting it from dependency trees.

Gabriel Stanovsky, Jessica Ficler, Ido Dagan, Yoav Goldberg• 2016

Related benchmarks

TaskDatasetResultRank
Open Information ExtractionCaRB (test)
F1 Score31.9
53
Open Information ExtractionOIE 2016 (test)
F154.38
32
Open Information ExtractionRe-OIE 2016 (test)
AUC43.3
20
Open Information ExtractionWEB
F158.9
18
Open Information ExtractionNYT
F1 Score37.2
18
Open Information ExtractionPENN
F1 Score39.1
18
Open Information ExtractionOIE 2016
F1 Score55.6
18
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