Combining classification with clustering for web person disambiguation

Web Person Disambiguation is often conducted through clustering web documents to identify different namesakes for a given name. This paper presents a new key-phrased clustering method combined with a second step re-classification to identify outliers to improve cluster performance. For document clustering, the hierarchical agglomerative approach is conducted based on the vector space model which uses key phrases as the main feature. Outliers of cluster results are then identified through a centroids-based method. The outliers are then reclassified by the SVM classifier into the more appropriate clusters using a key phrase-based string kernel model as its feature space. The re-classification uses the clustering result in the first step as its training data so as to avoid the use of separate training data required by most classification algorithms. Experiments conducted on the WePS-2 dataset show that the algorithm based on key phrases is effective in improving the WPD performance.