Weakly Supervised, Data-Driven Acquisition of Rules for Open Information Extraction

TitleWeakly Supervised, Data-Driven Acquisition of Rules for Open Information Extraction
Publication TypeConference Proceedings
Year of Publication2019
AuthorsGotti, F., and P. Langlais
Conference Name32nd Canadian Conference on Artificial Intelligence
Date Published06/2019
KeywordsNatural language processing, Open information extraction, Weak Supervision
AbstractWe propose a way to acquire rules for Open Information Extraction, based on lemma sequence patterns (including potential ty- pographical symbols) linking two named entities in a sentence. Rule acquisition is data-driven and requires little supervision. Given an arbitrary relation, we identify, in a large corpus, pairs of entities that are linked by the relation and then gather, score and rank other phrases that link the same entity pairs. We experimented with 81 relations and acquired 20 extraction rules for each by mining ClueWeb12. We devised a semi- automatic evaluation protocol to measure recall and precision and found them to be at most 79.9% and 62.4% respectively. Verbal patterns are of better quality than non-verbal ones, although the latter achieve a maximum recall of 76.5%. The strategy proposed does not necessitate expensive resources or time-consuming handcrafted resources, but does require a large amount of text.
NotesDataset available here : http://rali.iro.umontreal.ca/rali/sites/default/files/resources/misc/oie-paraphrase-rules.txt