@inproceedings{tan-etal-2020-neural,
    title = "Neural-{DINF}: A Neural Network based Framework for Measuring Document Influence",
    author = "Tan, Jie  and
      Yang, Changlin  and
      Li, Ying  and
      Tang, Siliang  and
      Huang, Chen  and
      Zhuang, Yueting",
    editor = "Jurafsky, Dan  and
      Chai, Joyce  and
      Schluter, Natalie  and
      Tetreault, Joel",
    booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
    month = jul,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/Author-page-Marten-During-lu/2020.acl-main.534/",
    doi = "10.18653/v1/2020.acl-main.534",
    pages = "6004--6009",
    abstract = "Measuring the scholarly impact of a document without citations is an important and challenging problem. Existing approaches such as Document Influence Model (DIM) are based on dynamic topic models, which only consider the word frequency change. In this paper, we use both frequency changes and word semantic shifts to measure document influence by developing a neural network framework. Our model has three steps. Firstly, we train the word embeddings for different time periods. Subsequently, we propose an unsupervised method to align vectors for different time periods. Finally, we compute the influence value of documents. Our experimental results show that our model outperforms DIM."
}