@inproceedings{sousa-etal-2026-extending,
    title = "Extending an Ensemble Baseline with Corpus-Based Graph Features for {P}ortuguese Pun Detection",
    author = "Sousa, Avelar Rodrigues de  and
      Sousa, Camilla Soares  and
      Barros, Carlos Henrique Santos  and
      Anchi{\^e}ta, Rafael Torres",
    editor = "Souza, Marlo  and
      de-Dios-Flores, Iria  and
      Santos, Diana  and
      Freitas, Larissa  and
      Souza, Jackson Wilke da Cruz  and
      Ribeiro, Eug{\'e}nio",
    booktitle = "Proceedings of the 17th International Conference on Computational Processing of {P}ortuguese ({PROPOR} 2026) - Vol. 1",
    month = apr,
    year = "2026",
    address = "Salvador, Brazil",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-dnd/2026.propor-1.75/",
    pages = "759--769",
    ISBN = "979-8-89176-387-6",
    abstract = "Automatic pun detection remains challenging because it depends on lexical ambiguity and contextual interaction, which are not explicitly captured by linear text representations. In Portuguese, TF-IDF-based ensemble methods provide competitive and interpretable baselines, but remain limited by surface-level features. This work investigates whether corpus-based graph information can complement such methods. Three graph representations are constructed from the Puntuguese corpus: a Co-occurrence graph, a PPMI-weighted graph, and a Pun-Context graph. In the current pipeline, each graph is converted into low-dimensional node embeddings with TruncatedSVD, which are then aggregated into document-level features and concatenated with TF-IDF representations in a soft-voting ensemble. Experimental results on the test set show that graph-based enrichment does not uniformly improve performance: Pun-Context and PPMI yield the strongest graph-augmented results, whereas combining all graphs degrades performance. These findings indicate that the usefulness of graph-based information depends strongly on how lexical relations are encoded and aggregated at the document level."
}