{'fraction_non_alphanumeric': 0.023636363636363636, 'fraction_numerical': 0.0, 'mean_word_length': 6.1558441558441555, '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}}
Evaluating QA Systems on Multiple Dimensions

Question-answering systems are expanding beyond information retrieval and information extraction, to become fullfledged, complex NLP applications. In this paper we discuss the evaluation of question-answering systems as complex NLP systems, and suggest three different dimensions for evaluation: objective or information-based evaluation; subjective evaluation; and architectural evaluation. We also discuss the role of ambiguity resolution in QA systems, and how ambiguity resolution might be evaluated.