@article{ImrZamGomJAMIAOpen2026,
    author = {Imran, Muhammad and Zamaraeva, Olga and G\'omez-Rodr\'iguez, Carlos},
    title = {{SynNER}: syntax-infused named entity recognition in the biomedical domain},
    journal = {JAMIA Open},
    volume = {9},
    number = {1},
    pages = {ooaf149},
    year = {2026},
    month = {02},
    abstract = {This study evaluates the usefulness of explicit syntactic knowledge, integrated via a neural mechanism, in improving the accuracy of named entity recognition in the domain of biomedical text processing.Syntactic structure of a text can be helpful to determine whether a certain part of the text is an entity or not. Parsing is an essential technique in natural language processing (NLP) that can be utilized to determine the syntactic structure of sentences in human languages. We propose to infuse syntactic knowledge through the attention mechanism using dependency parsing and sequence labelling parsing, as well as the multi-task learning paradigm. Experiments were conducted on five datasets: MTSamples, VAERS, NCBI-disease, BC2GM, and JNLPBA.We demonstrate improvements in the F1 score over the current state of the art on 3 out of 5 datasets (MTSamples, VAERS, and NCBI).We reduce the number of mismatches with gold labels in particular in the n-dash and parentheses tokens and in compound and adjective modifier dependencies.Syntactic features improve NER accuracy in attention-based neural systems, and parsing as sequence labelling brings additional benefits.Named Entity Recognition (NER) is a technology that helps computers automatically find and classify important terms in text, such as names of diseases, drugs, or medical procedures. This is especially valuable in the biomedical field, where researchers and clinicians need to process large volumes of text from scientific articles, clinical notes, or patient records. In this work, we present SynNER, a system that improves the accuracy of NER by teaching computers to pay attention not only to the words themselves, but also to syntax, ie, the internal structure of sentences. For example, recognizing how words are connected in a sentence (which parts of the sentence are subjects, objects or modifiers) can make it easier to correctly identify medical terms, even when they appear in complex contexts. We tested our method on five different collections of biomedical texts. The results showed that incorporating grammatical knowledge significantly boosted accuracy. Our SynNER system outperformed previous state-of-the-art methods on three of the five datasets. Our results show that using syntax can help researchers and healthcare professionals more reliably and quickly extract vital information from a vast amount of text, which could ultimately help improve biomedical research and clinical decision support tools.},
    issn = {2574-2531},
    doi = {10.1093/jamiaopen/ooaf149},
    url = {https://doi.org/10.1093/jamiaopen/ooaf149},
    eprint = {https://academic.oup.com/jamiaopen/article-pdf/9/1/ooaf149/67102987/ooaf149.pdf},
}