@inproceedings{wu-tsai-2018-cross,
    title = "Cross-language Article Linking Using Cross-Encyclopedia Entity Embedding",
    author = "Wu, Chun-Kai  and
      Tsai, Richard Tzong-Han",
    editor = "Walker, Marilyn  and
      Ji, Heng  and
      Stent, Amanda",
    booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/N18-2054/",
    doi = "10.18653/v1/N18-2054",
    pages = "334--339",
    abstract = "Cross-language article linking (CLAL) is the task of finding corresponding article pairs of different languages across encyclopedias. This task is a difficult disambiguation problem in which one article must be selected among several candidate articles with similar titles and contents. Existing works focus on engineering text-based or link-based features for this task, which is a time-consuming job, and some of these features are only applicable within the same encyclopedia. In this paper, we address these problems by proposing cross-encyclopedia entity embedding. Unlike other works, our proposed method does not rely on known cross-language pairs. We apply our method to CLAL between English Wikipedia and Chinese Baidu Baike. Our features improve performance relative to the baseline by 29.62{\%}. Tested 30 times, our system achieved an average improvement of 2.76{\%} over the current best system (26.86{\%} over baseline), a statistically significant result."
}