This paper presents an analysis of active learning strategies for sequence labeling tasks, such as information extraction and document segmentation. Active learning is particularly useful in natural language processing where unlabeled data is abundant but annotation is costly. The authors survey existing query selection strategies for sequence models and propose novel algorithms to address their limitations. They conduct a large-scale empirical comparison across multiple corpora, demonstrating that their proposed methods improve the state of the art. Active learning allows the learner to select which instances to label, aiming to achieve high accuracy with minimal labeling effort. Sequence labeling tasks, such as part-of-speech tagging, information extraction, and document segmentation, often involve annotating text, which can be time-consuming. While active learning has been studied for classification, it has received less attention for sequence labeling. The authors introduce several new query strategies for probabilistic sequence models, including information density, sequence vote entropy, and Fisher information. They also evaluate existing strategies such as uncertainty sampling and query-by-committee. Their empirical analysis shows that information density performs well across various tasks, often outperforming other methods. Sequence vote entropy is also effective, particularly in QBC settings. The study highlights the importance of considering both the informativeness of instances and their representativeness in the data distribution. The authors argue that normalization of entropy measures may bias the learner toward shorter sequences, which may not be optimal. They also note that while Fisher information is theoretically sound, its performance is inconsistent and often not superior to other methods. The paper concludes that information density, combined with uncertainty sampling, is a promising approach for active learning in sequence labeling tasks, especially for large corpora. The authors also discuss the computational costs of different strategies, noting that some methods are more expensive but may offer better performance in certain scenarios. Overall, the study provides a comprehensive evaluation of active learning strategies for sequence labeling, offering insights into their effectiveness and limitations.This paper presents an analysis of active learning strategies for sequence labeling tasks, such as information extraction and document segmentation. Active learning is particularly useful in natural language processing where unlabeled data is abundant but annotation is costly. The authors survey existing query selection strategies for sequence models and propose novel algorithms to address their limitations. They conduct a large-scale empirical comparison across multiple corpora, demonstrating that their proposed methods improve the state of the art. Active learning allows the learner to select which instances to label, aiming to achieve high accuracy with minimal labeling effort. Sequence labeling tasks, such as part-of-speech tagging, information extraction, and document segmentation, often involve annotating text, which can be time-consuming. While active learning has been studied for classification, it has received less attention for sequence labeling. The authors introduce several new query strategies for probabilistic sequence models, including information density, sequence vote entropy, and Fisher information. They also evaluate existing strategies such as uncertainty sampling and query-by-committee. Their empirical analysis shows that information density performs well across various tasks, often outperforming other methods. Sequence vote entropy is also effective, particularly in QBC settings. The study highlights the importance of considering both the informativeness of instances and their representativeness in the data distribution. The authors argue that normalization of entropy measures may bias the learner toward shorter sequences, which may not be optimal. They also note that while Fisher information is theoretically sound, its performance is inconsistent and often not superior to other methods. The paper concludes that information density, combined with uncertainty sampling, is a promising approach for active learning in sequence labeling tasks, especially for large corpora. The authors also discuss the computational costs of different strategies, noting that some methods are more expensive but may offer better performance in certain scenarios. Overall, the study provides a comprehensive evaluation of active learning strategies for sequence labeling, offering insights into their effectiveness and limitations.