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Interpretable Models for Information Extraction.

. University of Arizona, Tucson, USA, (2016)base-search.net (ftunivarizona:oai:repository.arizona.edu:10150/613348).

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Odinson: A Fast Rule-based Information Extraction Framework., , and . LREC, page 2183-2191. European Language Resources Association, (2020)Lightly-supervised Representation Learning with Global Interpretability., , , , and . SPNLP@NAACL-HLT, page 18-28. Association for Computational Linguistics, (2019)This before That: Causal Precedence in the Biomedical Domain., , , and . BioNLP@ACL, page 146-155. Association for Computational Linguistics, (2016)Sieve-based Coreference Resolution in the Biomedical Domain., , , and . LREC, European Language Resources Association (ELRA), (2016)Active Learning Design Choices for NER with Transformers., , , , and . LREC/COLING, page 321-334. ELRA and ICCL, (2024)Eidos, INDRA, & Delphi: From Free Text to Executable Causal Models., , , , , , , , , and 7 other author(s). NAACL-HLT (Demonstrations), page 42-47. Association for Computational Linguistics, (2019)Interpretable Models for Information Extraction.. University of Arizona, Tucson, USA, (2016)base-search.net (ftunivarizona:oai:repository.arizona.edu:10150/613348).Text Annotation Graphs: Annotating Complex Natural Language Phenomena., , , , and . LREC, European Language Resources Association (ELRA), (2018)Parsing as Tagging., , , and . LREC, page 5225-5231. European Language Resources Association, (2020)Swanson linking revisited: Accelerating literature-based discovery across domains using a conceptual influence graph., , and . ACL (System Demonstrations), page 103-108. Association for Computational Linguistics, (2017)