@inproceedings{dastgheib-asgari-2022-keyword,
    title = "Keyword-based Natural Language Premise Selection for an Automatic Mathematical Statement Proving",
    author = "Dastgheib, Doratossadat  and
      Asgari, Ehsaneddin",
    editor = "Ustalov, Dmitry  and
      Gao, Yanjun  and
      Panchenko, Alexander  and
      Valentino, Marco  and
      Thayaparan, Mokanarangan  and
      Nguyen, Thien Huu  and
      Penn, Gerald  and
      Ramesh, Arti  and
      Jana, Abhik",
    booktitle = "Proceedings of TextGraphs-16: Graph-based Methods for Natural Language Processing",
    month = oct,
    year = "2022",
    address = "Gyeongju, Republic of Korea",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/moar-dois/2022.textgraphs-1.14/",
    pages = "124--126",
    abstract = "Extraction of supportive premises for a mathematical problem can contribute to profound success in improving automatic reasoning systems. One bottleneck in automated theorem proving is the lack of a proper semantic information retrieval system for mathematical texts. In this paper, we show the effect of keyword extraction in the natural language premise selection (NLPS) shared task proposed in TextGraph-16 that seeks to select the most relevant sentences supporting a given mathematical statement."
}