Neural architectures for open-type relation argument extraction. (7th December 2018) - Roth, Benjamin; Conforti, Costanza; Poerner, Nina; Karn, Sanjeev Kumar; Schütze, Hinrich
Abstract: In this work, we focus on the task of open-type relation argument extraction (ORAE) : given a corpus, a query entity Q, and a knowledge base relation (e.g., " Q authored notable work with title X "), the model has to extract an argument of non-standard entity type (entities that cannot be extracted by a standard named entity tagger, for example, X : the title of a book or a work of art) from the corpus. We develop and compare a wide range of neural models for this task yielding large improvements over a strong baseline obtained with a neural question answering system. The impact of different sentence encoding architectures and answer extraction methods is systematically compared. An encoder based on gated recurrent units combined with a conditional random fields tagger yields the best results. We release a data set to train and evaluate ORAE, based on Wikidata and obtained by distant supervision.