2022/10/12 by Vera Provatorova, Simone Tedeschi, Provatorova, Vera +7
Computer Science · Decision Sciences · #Computation and Language (cs.CL) #Data Mining Algorithms and Applications #Data Quality and Management #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Topic Modeling
paper · pdf · doi:10.48550/arxiv.2210.06164
openalex publication_date 2022/10/12 · openalex created_date 2022/10/14 · openalex updated_date 2026/07/28
Entity disambiguation (ED) is the task of mapping an ambiguous entity mention to the corresponding entry in a structured knowledge base. Previous research showed that entity overshadowing is a significant challenge for existing ED models: when presented with an ambiguous entity mention, the models are much more likely to rank a more frequent yet less contextually relevant entity at the top. Here, we present NICE, an iterative approach that uses entity type information to leverage context and avoid over-relying on the frequency-based prior. Our experiments show that NICE achieves the best performance results on the overshadowed entities while still performing competitively on the frequent entities.