@inproceedings{yin-etal-2020-pod,
    title = "{P}o{D}: Positional Dependency-Based Word Embedding for Aspect Term Extraction",
    author = "Yin, Yichun  and
      Wang, Chenguang  and
      Zhang, Ming",
    editor = "Scott, Donia  and
      Bel, Nuria  and
      Zong, Chengqing",
    booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2020.coling-main.150/",
    doi = "10.18653/v1/2020.coling-main.150",
    pages = "1714--1719",
    abstract = "Dependency context-based word embedding jointly learns the representations of word and dependency context, and has been proved effective in aspect term extraction. In this paper, we design the positional dependency-based word embedding (PoD) which considers both dependency context and positional context for aspect term extraction. Specifically, the positional context is modeled via relative position encoding. Besides, we enhance the dependency context by integrating more lexical information (e.g., POS tags) along dependency paths. Experiments on SemEval 2014/2015/2016 datasets show that our approach outperforms other embedding methods in aspect term extraction."
}