Deep Neural Approaches to Relation Triplets Extraction: A Comprehensive Survey Tapas Nayaky, Navonil Majumder, Pawan Goyaly, Soujanya Poria y IIT Kharagpur, India Singapore University of Technology and Design, Singapore
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[email protected] Abstract contain a large number of triplets, they remain incomplete. On the other hand, relation triplets Recently, with the advances made in contin- can be automatically distilled from the copious uous representation of words (word embed- amount of free text on the Web. This can be lever- dings) and deep neural architectures, many re- search works are published in the area of rela- aged for identifying missing links in the existing tion extraction and it is very difficult to keep KBs or build a KB from scratch without human track of so many papers. To help future re- intervention. search, we present a comprehensive review There are two distinct research paradigms of of the recently published research works in relation extraction: open information extraction relation extraction. We mostly focus on re- (Open IE) and supervised relation extraction. lation extraction using deep neural networks which have achieved state-of-the-art perfor- Banko et al.(2007), Christensen et al.(2011), Et- mance on publicly available datasets. In this zioni et al.(2011), and Mausam et al.(2012) use survey, we cover sentence-level relation ex- open information extraction (Open IE) to extract traction to document-level relation extraction, relation triplets from sentences where relations set pipeline-based approaches to joint extraction is open. Open IE systems like KnowItAll (Etzioni approaches, annotated datasets to distantly su- et al., 2004), TEXTRUNNER (Yates et al., 2007), pervised datasets along with few very recent REVERB (Etzioni et al., 2011), SRLIE (Chris- research directions such as zero-shot or few- shot relation extraction, noise mitigation in tensen et al., 2011), and OLLIE (Mausam et al., distantly supervised datasets.