{'fraction_non_alphanumeric': 0.018148820326678767, 'fraction_numerical': 0.0, 'mean_word_length': 5.4186046511627906, '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 Entity Models on the TREC Question Answering Task

We propose entity models, a representation of the language used to describe a named entity (person, organization, or location). The model is purely statistical and constructed from snippets of text surrounding mentions of an entity. We evaluate the effectiveness of entity models for fact-based question answering. The results obtained on question answering are promising indicating that entity models contain useful information which would aid textual data mining and other related tasks.