ANEAR: Automatic Named Entity Aliasing Resolution

Abstract

Identifying the different aliases used by or for an entity is emerging as a significant problem in reliable Information Extraction systems, especially with the proliferation of social media and their ever growing impact on different aspects of modern life such as politics, finance, security, etc. In this paper, we address the novel problem of Named Entity Aliasing Resolution (NEAR). We attempt to solve the NEAR problem in a language-independent setting by extracting the different aliases and variants of person named entities. We generate feature vectors for the named entities by building co-occurrence models that use different weighting schemes. The aliasing resolution process applies unsupervised machine learning techniques over the vector space models in order to produce groups of entities along with their aliases. We test our approach on two languages: Arabic and English. We study the impact of varying the level of morphological preprocessing of the words, as well as the part of speech tags surrounding the person named entities, and the named entities’ distribution in the data set. We create novel evaluation data sets for both languages. NEAR yields better overall performance in Arabic than in English for comparable amounts of data, effectively using the POS tag information to improve performance. Our approach achieves an F _β = 1score of 67.85% and 70.03% for raw English and Arabic data sets, respectively.