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Semantic Analysis of Entity Contexts towards Open Named Entity Classification on the Web

This paper introduces the use of Latent Semantic Analysis (LSA) to uncover semantic structures/concepts hidden in entity contexts towards improving named entity recognition (NER) on the Web. The underlying idea of the paper is that words surrounding entities of the same category are potentially related to each other in one way or another. Analyzing such relations helps build implicit concepts around entity types, making entity contexts more discriminative, and avoiding data sparsity for a better classification. Our experiments on a Web data collection of entity contexts show that semantic analysis can give a significant error reduction.