Paper: Bipolar Person Name Identification of Topic Documents Using Principal Component Analysis

ACL ID C10-1020
Title Bipolar Person Name Identification of Topic Documents Using Principal Component Analysis
Venue International Conference on Computational Linguistics
Session Main Conference
Year 2010
Authors

In this paper, we propose an unsuper- vised approach for identifying bipolar person names in a set of topic documents. We employ principal component analysis (PCA) to discover bipolar word usage patterns of person names in the docu- ments and show that the signs of the en- tries in the principal eigenvector of PCA partition the person names into bipolar groups spontaneously. Empirical evalua- tions demonstrate the efficacy of the proposed approach in identifying bipolar person names of topics.