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EventNet-ITA: Italian Frame Parsing for Events

arXiv 2305.10892 ↗First posted 18 May 2023cs.CLcited by 0

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Abstract (arXiv)

This paper introduces EventNet-ITA, a large, multi-domain corpus annotated full-text with event frames for Italian. Moreover, we present and thoroughly evaluate an efficient multi-label sequence labeling approach for Frame Parsing. Covering a wide range of individual, social and historical phenomena, with more than 53,000 annotated sentences and over 200 modeled frames, EventNet-ITA constitutes the first systematic attempt to provide the Italian language with a publicly available resource for Frame Parsing of events, useful for a broad spectrum of research and application tasks. Our approach achieves a promising 0.9 strict F1-score for frame classification and 0.72 for frame element classification, on top of minimizing computational requirements. The annotated corpus and the frame parsing model are released under open license.

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References · 12

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2015Bidirectional LSTM-CRF Models for Sequence Taggingcited by 544
  3. 2016End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRFcited by 341
  4. 2021Structured Prediction as Translation between Augmented Natural Languagescited by 183
  5. 2021Text2Event: Controllable Sequence-to-Structure Generation for End-to-end Event Extractioncited by 129
  6. 2020Massive Choice, Ample Tasks (MaChAmp): A Toolkit for Multi-task Learning in NLPcited by 50
  7. 2018One for All: Neural Joint Modeling of Entities and Eventscited by 37
  8. 2017Frame-Semantic Parsing with Softmax-Margin Segmental RNNs and a Syntactic Scaffoldcited by 33
  9. 2018Syntactic Scaffolds for Semantic Structurescited by 27
  10. 2022SOCIOFILLMORE: A Tool for Discovering Perspectivescited by 5
  11. 2018Event Detection with Neural Networks: A Rigorous Empirical Evaluationcited by 3
  12. 2018Italian Event Detection Goes Deep Learningcited by 1

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