@inproceedings{ding-riloff-2018-human,
    title = "Human Needs Categorization of Affective Events Using Labeled and Unlabeled Data",
    author = "Ding, Haibo  and
      Riloff, Ellen",
    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 1 (Long Papers)",
    month = jun,
    year = "2018",
    address = "New Orleans, Louisiana",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/N18-1174/",
    doi = "10.18653/v1/N18-1174",
    pages = "1919--1929",
    abstract = "We often talk about events that impact us positively or negatively. For example {\textquotedblleft}I got a job{\textquotedblright} is good news, but {\textquotedblleft}I lost my job{\textquotedblright} is bad news. When we discuss an event, we not only understand its affective polarity but also the reason why the event is beneficial or detrimental. For example, getting or losing a job has affective polarity primarily because it impacts us financially. Our work aims to categorize affective events based upon human need categories that often explain people`s motivations and desires: PHYSIOLOGICAL, HEALTH, LEISURE, SOCIAL, FINANCIAL, COGNITION, and FREEDOM. We create classification models based on event expressions as well as models that use contexts surrounding event mentions. We also design a co-training model that learns from unlabeled data by simultaneously training event expression and event context classifiers in an iterative learning process. Our results show that co-training performs well, producing substantially better results than the individual classifiers."
}