Neural Open Information Extraction

By Lei Cui, Furu Wei, M. Zhou. 2018

167 citations recorded by the source corpus.

Conventional Open Information Extraction (Open IE) systems are usually built on hand-crafted patterns from other NLP tools such as syntactic parsing, yet they face problems of error propagation. In this paper, we propose a neural Open IE approach with an encoder-decoder framework. Distinct from existing methods, the neural Open IE approach learns highly confident arguments and relation tuples bootstrapped from a state-of-the-art Open IE system. An empirical study on a large benchmark dataset shows that the neural Open IE system significantly outperforms several baselines, while maintaining comparable computational efficiency.

Subfields: Artificial Intelligence

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Yejin Choi
2. Zhiyuan Liu
3. Maosong Sun
4. Mohit Bansal
5. Noah A. Smith
6. Xu Han
7. Shafiq R. Joty
8. Yankai Lin

Citations in the corpus

• Continual Few-shot Relation Learning via Embedding Space Regularization and Data Augmentation (2022, background): “There have been some efforts which focus on relation learning without predefined types, including open RE (Shinyama and Sekine, 2006; Etzioni et al., 2008; Cui et al., 2018; Gao et al., 2020) and continual relation learning (Wang et al.”
• Continual Relation Learning via Episodic Memory Activation and Reconsolidation (2020, background): “…et al., 2011; Mausam et al., 2012; Del Corro and Gemulla, 2013; Angeli et al., 2015; Petroni et al., 2015; Stanovsky and Da-gan, 2016; Mausam, 2016; Cui et al., 2018), relation discovery (Yao et al., 2011; Marcheggiani and Titov, 2016), relation clustering (Shinyama and Sekine, 2006; Elsahar et…”
• COMET: Commonsense Transformers for Automatic Knowledge Graph Construction (2019, background): “Other work in information extraction can also be applied to knowledge base construction with open-text entities (Soderland et al., 2010; Etzioni et al., 2011; Fader et al., 2011; Mausam et al., 2012; Fan et al., 2010; Cui et al., 2018), but these methods typically extract explicitly stated text rela”
• Prototypical Representation Learning for Relation Extraction (2021, unknown): “…Soares et al., 2019; Ye & Ling, 2019) and open-domain scenarios (Banko et al., 2007; Fader et al., 2011; Mausam et al., 2012; Del Corro & Gemulla, 2013; Angeli et al., 2015; Stanovsky & Dagan, 2016; Mausam, 2016; Cui et al., 2018; Shinyama & Sekine, 2006; Elsahar et al., 2017; Wu et al., 2019).”
• More Data, More Relations, More Context and More Openness: A Review and Outlook for Relation Extraction (2020, background): “…(1) Open information extraction (Open IE) , as shown in Figure 8, extracts relation phrases and arguments (entities) from text (Banko et al., 2007; Fader et al., 2011; Mausam et al., 2012; Del Corro and Gemulla, 2013; Angeli et al., 2015; Stanovsky and Dagan, 2016; Mausam, 2016; Cui et al., 2018).”
• A Comprehensive Survey on Automatic Knowledge Graph Construction (2023, background): “CopyAttention [186] includes a mechanism to copy words from input to output sequences via a neural bootstrapping strategy.”