Web People Search Disambiguation using Language Model Techniques
Juan Martínez-Romo, Dpto Lenguajes Y Sistemas, Lourdes Araujo, Dpto Lenguajes · 2015
In this paper we describe our participation in Web Peo-ple Search Clustering task. We present a new methodology based on language models to improve Web People Search disambiguation. In particular we introduce two different ap-proaches: One of them uses alternative weighting functions to represent a document and it apply a classical clustering algorithm. The second approach uses two sources of occu-pational information as reference collections and it applies an heuristic-based technique in order to resolve the number of different identities. Moreover, we have studied the impact in results of using stemming and a variant of Interpolated Aggregate Smoothing applied to language models.