@inproceedings{schoene-etal-2022-relate,
    title = "{RELATE}: Generating a linguistically inspired Knowledge Graph for fine-grained emotion classification",
    author = "Schoene, Annika Marie  and
      Dethlefs, Nina  and
      Ananiadou, Sophia",
    editor = "Calzolari, Nicoletta  and
      B{\'e}chet, Fr{\'e}d{\'e}ric  and
      Blache, Philippe  and
      Choukri, Khalid  and
      Cieri, Christopher  and
      Declerck, Thierry  and
      Goggi, Sara  and
      Isahara, Hitoshi  and
      Maegaard, Bente  and
      Mariani, Joseph  and
      Mazo, H{\'e}l{\`e}ne  and
      Odijk, Jan  and
      Piperidis, Stelios",
    booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference",
    month = jun,
    year = "2022",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2022.lrec-1.679/",
    pages = "6317--6327",
    abstract = "Several existing resources are available for sentiment analysis (SA) tasks that are used for learning sentiment specific embedding (SSE) representations. These resources are either large, common-sense knowledge graphs (KG) that cover a limited amount of polarities/emotions or they are smaller in size (e.g.: lexicons), which require costly human annotation and cover fine-grained emotions. Therefore using knowledge resources to learn SSE representations is either limited by the low coverage of polarities/emotions or the overall size of a resource. In this paper, we first introduce a new directed KG called {\textquoteleft}RELATE', which is built to overcome both the issue of low coverage of emotions and the issue of scalability. RELATE is the first KG of its size to cover Ekman`s six basic emotions that are directed towards entities. It is based on linguistic rules to incorporate the benefit of semantics without relying on costly human annotation. The performance of {\textquoteleft}RELATE' is evaluated by learning SSE representations using a Graph Convolutional Neural Network (GCN)."
}