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Semantic Role Labeling of NomBank Partitives

arXiv 2412.14328 ↗First posted 18 Dec 2024cs.CL cs.AIcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 12 of its 46 references that are in the map. First posted on arXiv 18 Dec 2024.

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References · 12

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,897
  3. 2019DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lightercited by 4,307
  4. 2018Deep contextualized word representationscited by 2,290
  5. 2019BERT Rediscovers the Classical NLP Pipelinecited by 1,059
  6. 2019What Does BERT Look At? An Analysis of BERT's Attentioncited by 1,003
  7. 2013Learning Factored Representations in a Deep Mixture of Expertscited by 278
  8. 2018Constituency Parsing with a Self-Attentive Encodercited by 199
  9. 2020Do Syntax Trees Help Pre-trained Transformers Extract Information?cited by 48
  10. 2016Neural Semantic Role Labeling with Dependency Path Embeddingscited by 15
  11. 2018A Span Selection Model for Semantic Role Labelingcited by 15
  12. 2018Evaluating the Utility of Hand-crafted Features in Sequence Labellingcited by 3

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