@inproceedings{ortiz-fuentes-etal-2026-attitude,
title = "Attitude Analysis in Systemic Functional Linguistics: A New Corpus and Benchmark Task",
author = "Ortiz-Fuentes, Jorge and
Bravo-Marquez, Felipe and
Quiroz, Beatriz",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1321/",
doi = "10.18653/2026.findings-emnlp.1321",
pages = "28000--28023",
ISBN = "979-8-89176-462-0",
abstract = "We introduce a corpus for Systemic Functional Linguistics (SFL) Appraisal analysis in Chilean Spanish, together with TULIO, a BERT model adapted to Chilean Spanish through continual pre-training. The corpus contains 2,546 texts annotated for the Attitude subsystem, with a focus on negative Judgment, and supports two span-level sequence labeling tasks: Attitude Types, which distinguishes Affect, Judgment, and Appreciation, and Judgment Subtypes, which identifies five finer-grained forms of social evaluation. To reduce the boundary ambiguity reported in prior Appraisal annotation work, we use a two-stage protocol that first marks structural spans and then assigns Attitude labels. Three expert annotators reach almost perfect agreement at the general level (Krippendorff{'}s {\ensuremath{\alpha}} = 0.834) and substantial agreement for Judgment Subtypes ({\ensuremath{\alpha}} = 0.647). We benchmark supervised sequence labelers and structured LLM agents. The highest observed F1 scores are 0.554 on Attitude Types from a Flair BiLSTM-CRF with TULIO embeddings and 0.410 on Judgment Subtypes from Qwen 3.6 Plus. Under Twitter-only training, supervised performance is genre-sensitive, with higher F1 on Twitter than on other genres. All model scores remain below expert agreement."
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<abstract>We introduce a corpus for Systemic Functional Linguistics (SFL) Appraisal analysis in Chilean Spanish, together with TULIO, a BERT model adapted to Chilean Spanish through continual pre-training. The corpus contains 2,546 texts annotated for the Attitude subsystem, with a focus on negative Judgment, and supports two span-level sequence labeling tasks: Attitude Types, which distinguishes Affect, Judgment, and Appreciation, and Judgment Subtypes, which identifies five finer-grained forms of social evaluation. To reduce the boundary ambiguity reported in prior Appraisal annotation work, we use a two-stage protocol that first marks structural spans and then assigns Attitude labels. Three expert annotators reach almost perfect agreement at the general level (Krippendorff’s \ensuremathα = 0.834) and substantial agreement for Judgment Subtypes (\ensuremathα = 0.647). We benchmark supervised sequence labelers and structured LLM agents. The highest observed F1 scores are 0.554 on Attitude Types from a Flair BiLSTM-CRF with TULIO embeddings and 0.410 on Judgment Subtypes from Qwen 3.6 Plus. Under Twitter-only training, supervised performance is genre-sensitive, with higher F1 on Twitter than on other genres. All model scores remain below expert agreement.</abstract>
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%0 Conference Proceedings
%T Attitude Analysis in Systemic Functional Linguistics: A New Corpus and Benchmark Task
%A Ortiz-Fuentes, Jorge
%A Bravo-Marquez, Felipe
%A Quiroz, Beatriz
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F ortiz-fuentes-etal-2026-attitude
%X We introduce a corpus for Systemic Functional Linguistics (SFL) Appraisal analysis in Chilean Spanish, together with TULIO, a BERT model adapted to Chilean Spanish through continual pre-training. The corpus contains 2,546 texts annotated for the Attitude subsystem, with a focus on negative Judgment, and supports two span-level sequence labeling tasks: Attitude Types, which distinguishes Affect, Judgment, and Appreciation, and Judgment Subtypes, which identifies five finer-grained forms of social evaluation. To reduce the boundary ambiguity reported in prior Appraisal annotation work, we use a two-stage protocol that first marks structural spans and then assigns Attitude labels. Three expert annotators reach almost perfect agreement at the general level (Krippendorff’s \ensuremathα = 0.834) and substantial agreement for Judgment Subtypes (\ensuremathα = 0.647). We benchmark supervised sequence labelers and structured LLM agents. The highest observed F1 scores are 0.554 on Attitude Types from a Flair BiLSTM-CRF with TULIO embeddings and 0.410 on Judgment Subtypes from Qwen 3.6 Plus. Under Twitter-only training, supervised performance is genre-sensitive, with higher F1 on Twitter than on other genres. All model scores remain below expert agreement.
%R 10.18653/2026.findings-emnlp.1321
%U https://aclanthology.org/2026.findings-emnlp.1321/
%U https://doi.org/10.18653/2026.findings-emnlp.1321
%P 28000-28023
Markdown (Informal)
[Attitude Analysis in Systemic Functional Linguistics: A New Corpus and Benchmark Task](https://aclanthology.org/2026.findings-emnlp.1321/) (Ortiz-Fuentes et al., Findings 2026)
ACL