Towards Semantic Integration of Opinions: Unified Opinion Concepts Ontology and Extraction Task
It addresses the challenge of semantic opinion integration for NLP researchers, but it is incremental as it builds on existing facets and structures.
This paper tackles the problem of integrating opinions in NLP by introducing the Unified Opinion Concepts (UOC) ontology and a new extraction task (UOCE), establishing baseline performance with state-of-the-art generative models.
This paper introduces the Unified Opinion Concepts (UOC) ontology to integrate opinions within their semantic context. The UOC ontology bridges the gap between the semantic representation of opinion across different formulations. It is a unified conceptualisation based on the facets of opinions studied extensively in NLP and semantic structures described through symbolic descriptions. We further propose the Unified Opinion Concept Extraction (UOCE) task of extracting opinions from the text with enhanced expressivity. Additionally, we provide a manually extended and re-annotated evaluation dataset for this task and tailored evaluation metrics to assess the adherence of extracted opinions to UOC semantics. Finally, we establish baseline performance for the UOCE task using state-of-the-art generative models.