Bayesian Non-Exhaustive Classification for Active Online Name Disambiguation

Œe name disambiguation task partitions a collection of records pertaining to a given name, such that there is a one-to-one correspondence between the partitions and a group of people, all sharing that given name. Most existing solutions for this task are proposed for static data. However, more realistic scenarios stipulate emergence of records in a streaming fashion where records may belong to known as well as unknown persons all sharing the same name. Œis requires a flexible name disambiguation algorithm that can not only classify records of known persons represented in the training data by their existing records but can also identify records of new ambiguous personswith no existing records included in the initial training dataset. Toward achieving this objective, in this paper we propose a Bayesian non-exhaustive classification framework for solving online name disambiguation. In particular, we present a Dirichlet Process GaussianMixtureModel (DPGMM) as a core engine for online name disambiguation task. Meanwhile, two online inference algorithms, namely one-pass Gibbs sampler and Sequential Importance Sampling with Resampling (also known as particle filtering), are proposed to simultaneously perform online classification and new class discovery. 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 significantly beŠer than existing methods for performing online name disambiguation task. We also propose an interactive version of our online name disambiguation method designed to leverage user feedback to improve prediction accuracy. ACM Reference format: Baichuan Zhang, Murat Dundar, andMohammad Al Hasan. 2016. Bayesian Non-Exhaustive Classification for Active Online Name Disambiguation. In Proceedings of ACM Conference, Washington, DC, USA, July 2017 (Confer-