@inproceedings{zhang-etal-2018-pubse,
    title = "{P}ub{SE}: A Hierarchical Model for Publication Extraction from Academic Homepages",
    author = "Zhang, Yiqing  and
      Qi, Jianzhong  and
      Zhang, Rui  and
      Yin, Chuandong",
    editor = "Riloff, Ellen  and
      Chiang, David  and
      Hockenmaier, Julia  and
      Tsujii, Jun{'}ichi",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/D18-1123/",
    doi = "10.18653/v1/D18-1123",
    pages = "1005--1010",
    abstract = "Publication information in a researcher{'}s academic homepage provides insights about the researcher{'}s expertise, research interests, and collaboration networks. We aim to extract all the publication strings from a given academic homepage. This is a challenging task because the publication strings in different academic homepages may be located at different positions with different structures. To capture the positional and structural diversity, we propose an end-to-end hierarchical model named PubSE based on Bi-LSTM-CRF. We further propose an alternating training method for training the model. Experiments on real data show that PubSE outperforms the state-of-the-art models by up to 11.8{\%} in F1-score."
}