Neural architectures for open-type relation argument extraction

Roth, Benjamin; Conforti, Costanza; Poerner, Nina; Schütze, Hinrich und Kumar Karn, Sanjeev (7. December 2018): Neural architectures for open-type relation argument extraction. In: Natural Language Engineering, Vol. 25, No. 2: pp. 219-238

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.

• Item Type: Journal article
• Research Centers: Center for Information and Language Processing (CIS)
• Subjects: 000 Computer science, information and general works > 000 Computer science, knowledge, and systems; 400 Language > 400 Language
• Language: English