• Refine your search:
  • 
    Keyword:
    
    • Computational Linguistics (1)
    • Information Extraction (1)
    • Machine Learning (1)
    • Machine Learning and Data Mining (1)
    • Natural Language Processing (1)
  • 
    Creator / Publisher:
    
    • Guo, Zhijiang (1)
    • Lu, Wei (1)
    • Nan, Guoshun (1)
    • Qiao, Rui (1)
    • The 2021 Conference on Empirical Methods in Natural Language Processing 2021 (1)
    • Zeng, Jiaqi (1)
  • 
    Year
  • 
    Medium
  • 
    Type:
    
    • Article (1)
  • 
    BLLDB-Access:
    • free (1)
    • subject to license (0)

Search in the Catalogues and Directories

All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 1 of 1 1 Uncovering Main Causalities for Long-tailed Information Extraction ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Guo, Zhijiang; Lu, Wei; Nan, Guoshun; Qiao, Rui; Zeng, Jiaqi. - : Underline Science Inc., 2021 Abstract: Anthology paper link: https://aclanthology.org/2021.emnlp-main.763/ Abstract: Information Extraction (IE) aims to extract structural information from unstructured texts. In practice, long-tailed distributions caused by the selection bias of a dataset, may lead to incorrect correlations, also known as spurious correlations, between entities and labels in the conventional likelihood models. This motivates us to propose counterfactual IE (CFIE), a novel framework that aims to uncover the main causalities behind data in the view of causal inference. Specifically, 1) we first introduce a unified structural causal model (SCM) for various IE tasks, describing the relationships among variables; 2) with our SCM, we then generate counterfactuals based on an explicit language structure to better calculate the direct causal effect during the inference stage; 3) we further propose a novel debiasing approach to yield more robust predictions. Experiments on three IE tasks across five public datasets show the effectiveness ... Keyword: Computational Linguistics; Information Extraction; Machine Learning; Machine Learning and Data Mining; Natural Language Processing URL: https://underline.io/lecture/37638-uncovering-main-causalities-for-long-tailed-information-extraction https://dx.doi.org/10.48448/53zx-4546 BASE Hide details

Mobile view All Catalogues UB Frankfurt Linguistik 0 IDS Mannheim 0 OLC Linguistik 0 UB Frankfurt Retrokatalog 0 DNB Subject Category Language 0 Institut für Empirische Sprachwissenschaft 0 Leibniz-Centre General Linguistics (ZAS) 0 Bibliographies BLLDB 0 BDSL 0 IDS Bibliografie zur deutschen Grammatik 0 IDS Bibliografie zur Gesprächsforschung 0 IDS Konnektoren im Deutschen 0 IDS Präpositionen im Deutschen 0 IDS OBELEX meta 0 MPI-SHH Linguistics Collection 0 MPI for Psycholinguistics 0 Linked Open Data catalogues Annohub 0 Online resources Link directory 0 Journal directory 0 Database directory 0 Dictionary directory 0 Open access documents BASE 1 Linguistik-Repository 0 IDS Publikationsserver 0 Online dissertations 0 Language Description Heritage 0