{'fraction_non_alphanumeric': 0.02544529262086514, 'fraction_numerical': 0.013994910941475827, 'mean_word_length': 4.786764705882353, '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}}
Contextual Approach for Paragraph Selection in Question Answering Task

This year we participated in the English monolingual paragraph 
selection task at ResPubliQA 2010. Our general strategy is to find the 
supporting word context from query and candidate passages during the 
paragraph selection. We use the techniques of state-of-the art publicly available 
question answering systems, i.e. Open Ephyra and JIRS, and the random 
projection implementation in the Semantic Vectors package to evaluate the 
word context. To strengthen the paragraph selection, besides the context 
evaluation, we also use n-gram overlapping and textual containment. Our 
approach has a c@1 measure of 0.73 for our pattern-based context 
configuration and 0.64 for our n-gram-based context configuration.