We present a technique for augmenting annotated training data with hierarchical word clusters that are automatically derived from a large unannotated corpus. Cluster membership is encoded in features that are incorporated in a discriminatively trained tagging model. Active learning is used to select training examples. We evaluate the technique for named-entity tagging. Compared with a state-of-the-art HMM-based name finder, the presented technique requires only 13% as much annotated data to achieve the same level of performance. Given a large annotated training set of 1,000,000 words, the technique achieves a 25% reduction in error over the state-of-the-art HMM trained on the same material.
REF augmented name tagging training data with hierarchical word clusters and encoded cluster membership in features for improving name tagging.
Name Tagging With Word Clusters And Discriminative Training
Human Language Technology Conference And Meeting Of The North American Association For Computational Linguistics
Computer Science