In this work, we introduce 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, e.g., 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.
Another similar approach is introduced by REF , which performs relation argument extraction given a query entity and relation, essentially not for joint triplet extraction.
Neural Architectures for Open-Type Relation Argument Extraction
Nat. Lang. Eng. 25 (2019) 219-238
Computer Science