Semantic Role Labeling of NomBank Partitives

• URL: http://arxiv.org/abs/2412.14328v2
• Date: Fri, 20 Dec 2024 16:17:20 GMT
• Title: Semantic Role Labeling of NomBank Partitives
• Authors: Adam Meyers, Advait Pravin Savant, John E. Ortega,
• Abstract summary: Several systems are described using traditional and transformer-based machine learning, as well as ensembling.<n>Our highest scoring system achieves an F1 of 91.74% using "gold" parses from the Penn Treebank and 91.12% when using the Berkeley Neural.
• Score: 2.867517731896504
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This article is about Semantic Role Labeling for English partitive nouns (5%/REL of the price/ARG1; The price/ARG1 rose 5 percent/REL) in the NomBank annotated corpus. Several systems are described using traditional and transformer-based machine learning, as well as ensembling. Our highest scoring system achieves an F1 of 91.74% using "gold" parses from the Penn Treebank and 91.12% when using the Berkeley Neural parser. This research includes both classroom and experimental settings for system development.

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