{'fraction_non_alphanumeric': 0.019144144144144143, 'fraction_numerical': 0.021396396396396396, 'mean_word_length': 4.966442953020135, '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}}
Answering Natural Language Questions with Intui3

Intui3 is one of the participating systems at the fourth evaluation campaign on multilingual question answering over linked data, QALD4. The system accepts as input a question formulated in natural language (in English), and uses syntactic and semantic information to construct its interpretation with respect to a given database of RDF triples (in this case DBpedia 3.9). The interpretation is mapped to the corresponding SPARQL query, which is then run against a SPARQL endpoint to retrieve the answers to the initial question. Intui3 competed in the challenge called Task 1: Multilingual question answering over linked data, which offered 200 training questions and 50 test questions in 7 different languages. It obtained an F-measure of 0.24 by providing a correct answer to 10 of the test questions and a partial answer to 4 of them.