Neural architectures for open-type relation argument extraction. (7th December 2018)

• Record Type: Journal Article
• Authors: Roth, Benjamin; Conforti, Costanza; Poerner, Nina; Karn, Sanjeev Kumar; Schütze, Hinrich
• Is Part Of: Natural language engineering. Volume 25:Part 2(2019)
• Page Start: 219
• Page End: 238
• Publication Date: 2018-12-07
• Subjects: information extraction, -- machine learning, -- question answering, -- text data mining; Natural language processing (Computer science) -- Periodicals; Software engineering -- Periodicals; 006.35
• DOI: 10.1017/S1351324918000451
• Languages: English
• ISSNs: 1351-3249

Abstract

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.