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Semantic Lexicon Construction: Learning from Unlabeled Data via Spectral Analysis

This paper considers the task of automatically collecting words with their entity class labels, starting from a small number of labeled examples (`seed' words). We show that spectral analysis is useful for compensating for the paucity of labeled examples by learning from unlabeled data. The proposed method significantly outperforms a number of methods that employ techniques such as EM and co-training. Furthermore, when trained with 300 labeled examples and unlabeled data, it rivals Naive Bayes classifiers trained with 7500 labeled examples.