RELATE: Generating a linguistically inspired Knowledge Graph for fine-grained emotion classification
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 ‘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 ‘RELATE’ is evaluated by learning SSE representations using a Graph Convolutional Neural Network (GCN).- Anthology ID:
- 2022.lrec-1.679
- Volume:
- Proceedings of the Thirteenth Language Resources and Evaluation Conference
- Month:
- June
- Year:
- 2022
- Address:
- Marseille, France
- Editors:
- Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Jan Odijk, Stelios Piperidis
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association
- Note:
- Pages:
- 6317–6327
- Language:
- URL:
- https://aclanthology.org/2022.lrec-1.679
- DOI:
- Cite (ACL):
- Annika Marie Schoene, Nina Dethlefs, and Sophia Ananiadou. 2022. RELATE: Generating a linguistically inspired Knowledge Graph for fine-grained emotion classification. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 6317–6327, Marseille, France. European Language Resources Association.
- Cite (Informal):
- RELATE: Generating a linguistically inspired Knowledge Graph for fine-grained emotion classification (Schoene et al., LREC 2022)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/2022.lrec-1.679.pdf