Knowledge-Based Weak Supervision for Information Extraction of Overlapping Relations

By Raphael Hoffmann, Congle Zhang, Xiao Ling, Luke S. Zettlemoyer, Daniel S. Weld. 2011

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In Natural Language Processing

Citations in the corpus

TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension (2017, background): “For example, our data would also support multi-instance learning, which makes the at least once assumption , from relation extraction (Riedel et al., 2010; Hoffmann et al., 2011) or many other possibilities.”
Semantic Parsing on Freebase from Question-Answer Pairs (2013, background): “Sev- 8Oracle score is the fraction of examples for which D̃(x) contains any derivation with the correct denotation. eral works perform relation extraction using distant supervision from a knowledge base (Riedel et al., 2010; Carlson et al., 2010; Hoffmann et al., 2011; Surdeanu et al., 2012).”
Zero-Shot Relation Extraction via Reading Comprehension (2017, methodology): “When the type of facts (relations) are predeﬁned, one can use crowdsourcing (Liu et al., 2016) or distant supervision (Hoffmann et al., 2011) to collect examples and train an extraction model for each relation type.”
Training Classifiers with Natural Language Explanations (2018, methodology): “, 2009) and multi-instance learning (Riedel et al., 2010; Hoffmann et al., 2011), an existing knowledge base is used to (probabilistically) impute a training set.”
Knowledge Graph Embedding by Translating on Hyperplanes (2014, methodology): “Most existing extracting methods (Mintz et al. 2009; Riedel, Yao, and McCallum 2010; Hoffmann et al. 2011; Surdeanu et al. 2012) distantly collect evidences from an external text corpus for a candidate fact, ignoring the capability of the knowledge graph itself to reason the new fact.”
Learning Entity and Relation Embeddings for Knowledge Graph Completion (2015, methodology): “Most exiting methods (Mintz et al. 2009; Riedel, Yao, and McCallum 2010; Hoffmann et al. 2011; Surdeanu et al. 2012) take knowledge graphs as distant supervision to automatically annotate sentences in large-scale text corpora as training instances, and then extract textual features to build relation”
Continual Relation Learning via Episodic Memory Activation and Reconsolidation (2020, background): “…2015; Liu et al., 2015; Miwa and Bansal, 2016) and distantly supervised models (Bunescu and Mooney, 2007; Mintz et al., 2009; Riedel et al., 2010; Hoffmann et al., 2011; Zeng et al., 2015; Lin et al., 2016; Han et al., 2018a; Baldini Soares et al., 2019), focuses on extracting predefined…”
FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation (2018, background): “Adopting distant supervision is a primary approach to alleviate this problem for RC (Mintz et al.; Riedel et al.; Hoffmann et al., 2011; Surdeanu et al., 2012; Zeng et al., 2015; Lin et al., 2016), which heuristically aligns knowledge bases (KBs) and text to automatically annotate adequate amounts…”
Matching the Blanks: Distributional Similarity for Relation Learning (2019, background): “Unlike related work in distant supervision for information extraction (Hoffmann et al., 2011; Mintz et al., 2009), we do not use relation labels at training time.”
Injecting Logical Background Knowledge into Embeddings for Relation Extraction (2015, background): “First, for canonical relations it effectively performs distant supervision (Bunescu and Mooney, 2007; Mintz et al., 2009; Yao et al., 2011; Hoffmann et al., 2011; Surdeanu et al., 2012) and hence requires no textual annotations.”
Open Question Answering with Weakly Supervised Embedding Models (2014, background): “Initial works attempting to learn to connect KBs and natural language with less supervision have actually been tackling the information extraction problem [16,11,14,19].”
Representing Text for Joint Embedding of Text and Knowledge Bases (2015, background): “Other work proposed more sophisticated models that reason about sentence-level hidden variables (Riedel et al., 2010; Hoffmann et al., 2011; Surdeanu et al., 2012) or model the noise arising from the incompleteness of knowledge bases and text collections (Ritter et al., 2013), inter alia .”
NaturalLI: Natural Logic Inference for Common Sense Reasoning (2014, background): “Prominent examples include question answering (Voorhees, 2001), semantic parsing (Zelle and Mooney, 1996; Zettlemoyer and Collins, 2007; Kwiatkowski et al., 2013; Berant and Liang, 2014), and information extraction systems (Hoffmann et al., 2011; Surdeanu et al., 2012).”
Neural Snowball for Few-Shot Relation Learning (2019, background): “To alleviate wrong labeling, Riedel et al. (2010); Hoffmann et al. (2011) model distant supervision as a multi-instance multi-label task.”
Large-scale Semantic Parsing without Question-Answer Pairs (2014, background): “Recent approaches in relation extraction use distant supervision from a knowledge base to predict grounded relations between two target entities (Mintz et al., 2009; Hoffmann et al., 2011; Riedel et al., 2013).”
Towards Understanding Gender Bias in Relation Extraction (2019, background): “Great efforts have been made to improve NRE models by mitigating the effects of noise in the training data introduced by Distant Supervision (Hoffmann et al., 2011; Surdeanu et al., 2012; Lin et al., 2016; Liu et al., 2017; Feng et al., 2018; Qin et al., 2018).”
Neural Ranking Models with Weak Supervision (2017, background): “Weak supervision is a natural way to benefit from unsupervised data and it has been applied in NLP for various tasks including relation extraction [3, 15], knowledge-base completion [17], sentiment analysis [34], etc.”