@inproceedings{phi-etal-2024-polynere,
    title = "{P}oly{NERE}: A Novel Ontology and Corpus for Named Entity Recognition and Relation Extraction in Polymer Science Domain",
    author = "Phi, Van-Thuy  and
      Teranishi, Hiroki  and
      Matsumoto, Yuji  and
      Oka, Hiroyuki  and
      Ishii, Masashi",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.1126",
    pages = "12856--12866",
    abstract = "Polymers are widely used in diverse fields, and the demand for efficient methods to extract and organize information about them is increasing. An automated approach that utilizes machine learning can accurately extract relevant information from scientific papers, providing a promising solution for automating information extraction using annotated training data. In this paper, we introduce a polymer-relevant ontology featuring crucial entities and relations to enhance information extraction in the polymer science field. Our ontology is customizable to adapt to specific research needs. We present PolyNERE, a high-quality named entity recognition (NER) and relation extraction (RE) corpus comprising 750 polymer abstracts annotated using our ontology. Distinctive features of PolyNERE include multiple entity types, relation categories, support for various NER settings, and the ability to assert entities and relations at different levels. PolyNERE also facilitates reasoning in the RE task through supporting evidence. While our experiments with recent advanced methods achieved promising results, challenges persist in adapting NER and RE from abstracts to full-text paragraphs. This emphasizes the need for robust information extraction systems in the polymer domain, making our corpus a valuable benchmark for future developments.",
}

Polymers are widely used in diverse fields, and the demand for efficient methods to extract and organize information about them is increasing. An automated approach that utilizes machine learning can accurately extract relevant information from scientific papers, providing a promising solution for automating information extraction using annotated training data. In this paper, we introduce a polymer-relevant ontology featuring crucial entities and relations to enhance information extraction in the polymer science field. Our ontology is customizable to adapt to specific research needs. We present PolyNERE, a high-quality named entity recognition (NER) and relation extraction (RE) corpus comprising 750 polymer abstracts annotated using our ontology. Distinctive features of PolyNERE include multiple entity types, relation categories, support for various NER settings, and the ability to assert entities and relations at different levels. PolyNERE also facilitates reasoning in the RE task through supporting evidence. While our experiments with recent advanced methods achieved promising results, challenges persist in adapting NER and RE from abstracts to full-text paragraphs. This emphasizes the need for robust information extraction systems in the polymer domain, making our corpus a valuable benchmark for future developments.
Phi, Van-Thuy Teranishi, Hiroki Matsumoto, Yuji Oka, Hiroyuki Ishii, Masashi
PolyNERE: A Novel Ontology and Corpus for Named Entity Recognition and Relation Extraction in Polymer Science Domain