@inproceedings{shiue-etal-2018-ntu,
    title = "{NTU} {NLP} Lab System at {S}em{E}val-2018 Task 10: Verifying Semantic Differences by Integrating Distributional Information and Expert Knowledge",
    author = "Shiue, Yow-Ting  and
      Huang, Hen-Hsen  and
      Chen, Hsin-Hsi",
    editor = "Apidianaki, Marianna  and
      Mohammad, Saif M.  and
      May, Jonathan  and
      Shutova, Ekaterina  and
      Bethard, Steven  and
      Carpuat, Marine",
    booktitle = "Proceedings of the 12th International Workshop on Semantic Evaluation",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/S18-1171/",
    doi = "10.18653/v1/S18-1171",
    pages = "1027--1033",
    abstract = "This paper presents the NTU NLP Lab system for the SemEval-2018 Capturing Discriminative Attributes task. Word embeddings, pointwise mutual information (PMI), ConceptNet edges and shortest path lengths are utilized as input features to build binary classifiers to tell whether an attribute is discriminative for a pair of concepts. Our neural network model reaches about 73{\%} F1 score on the test set and ranks the 3rd in the task. Though the attributes to deal with in this task are all visual, our models are not provided with any image data. The results indicate that visual information can be derived from textual data."
}