{'fraction_non_alphanumeric': 0.008676789587852495, 'fraction_numerical': 0.0, 'mean_word_length': 5.93984962406015, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Active Learning with Perceptron for Structured Output

Typically, structured output scenarios are characterized by a high cost associated with obtaining supervised training data, motivating the study of active learning protocols for these situations. Starting with active learning approaches for multiclass classification, we first design querying functions for selecting entire structured instances, exploring the tradeoff between selecting instances based on a global margin or a combination of the margin of local classifiers. We then look at the setting where subcomponents of the structured instance can be queried independently and examine the benefit of incorporating structural information for active learning in such scenarios. Empirical results using these querying functions on both synthetic data and the semantic role labeling task demonstrate a significant reduction in the need for supervised training data.