{'fraction_non_alphanumeric': 0.01609657947686117, 'fraction_numerical': 0.0, 'mean_word_length': 5.21875, '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}}
Facts That Matter: Dynamic Fact Retrieval for Entity-Centric Search Queries

Entity-centric queries constitute a significant proportion of all search queries processed by the popular search engines. Answering such queries often involves selecting facts pertaining to an entity from an underlying knowledge graph. Prior work on this draws on hand-crafted features that require scanning the entire knowledge graph beforehand. Instead, we propose a neural method that exploits the linguistic and semi-linguistic nature of the entity search queries and the facts, and can hence be applied dynamically to entirely new sets of candidate facts. We optimize our model using a pairwise loss function to correctly predict the relevance and importance scores for each fact for a given query entity, while the overall fact ranking is based on a linear combination of these scores. We show that our simple approach outperforms previous work, ensuring better fact retrieval for entity-centric search queries.