Hidden Markov Model} Induction by Bayesian Model Merging
Advances in Neural Information Processing Systems 5, [NIPS Conference]
Information Extraction with HMM Structures Learned by Stochastic Optimization
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Nymble: a high-performance learning name-finder
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
Hi-index | 0.00 |
In this paper we explore the use of hidden Markov models on the task of role identification from free text. Role identification is an important stage of the information extraction process, assigning roles to particular types of entities with respect to a particular event. Hidden Markov models (HMMs) have been shown to achieve good performance when applied to information extraction tasks in both semistructured and free text. The main contribution of this work is the analysis of whether and how linguistic processing of textual data can improve the extraction performance of HMMs. The emphasis is on the minimal use of computationally expensive linguistic analysis. The overall conclusion is that the performance of HMMs is still worse than an equivalent manually constructed system. However, clear paths for improvement of the method are shown, aiming at a method, which is easily adaptable to new domains.