Bayesian Non-Exhaustive Classification A Case Study: Online Name Disambiguation using Temporal Record Streams
This addresses the need for real-time disambiguation in domains like bibliographic data, but it is incremental as it builds on existing batch methods by adding online capabilities.
The paper tackles the problem of online name disambiguation, where records must be partitioned for multiple persons in real-time while identifying new entities without prior records, and demonstrates that the proposed Bayesian non-exhaustive classification method outperforms existing methods in experiments.
The name entity disambiguation task aims to partition the records of multiple real-life persons so that each partition contains records pertaining to a unique person. Most of the existing solutions for this task operate in a batch mode, where all records to be disambiguated are initially available to the algorithm. However, more realistic settings require that the name disambiguation task be performed in an online fashion, in addition to, being able to identify records of new ambiguous entities having no preexisting records. In this work, we propose a Bayesian non-exhaustive classification framework for solving online name disambiguation task. Our proposed method uses a Dirichlet process prior with a Normal * Normal * Inverse Wishart data model which enables identification of new ambiguous entities who have no records in the training data. For online classification, we use one sweep Gibbs sampler which is very efficient and effective. As a case study we consider bibliographic data in a temporal stream format and disambiguate authors by partitioning their papers into homogeneous groups. Our experimental results demonstrate that the proposed method is better than existing methods for performing online name disambiguation task.