Modeling Relations and Their Mentions without Labeled Text

By Sebastian Riedel, Limin Yao, Andrew McCallum. 2010

1,470 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Molecular Biology, Information Retrieval & Search

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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).”
Snorkel: Rapid Training Data Creation with Weak Supervision (2017, background): “For example, if a user provides two knowledge bases for distant supervision, how should a data point that matches only one knowledge base be labeled? Some researchers have used multi-instance learning to reduce the noise in weak supervision sources [22,41], essentially modeling the different weak su”
Training Classifiers with Natural Language Explanations (2018, methodology): “In distant supervision (Craven et al., 1999; Mintz et al., 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.”
GLM-130B: An Open Bilingual Pre-trained Model (2022, methodology): “For example, CoNLL04 only contains five different relations; the most diverse NYT dataset contains 24 Freebase predicates.”
Constructing Datasets for Multi-hop Reading Comprehension Across Documents (2017, neutral): “Finally, although our task is sig-niﬁcantly more complex than most previous tasks where distant supervision has been applied, the distant supervision assumption is only violated for 20% of the samples – a proportion similar to previous work (Riedel et al., 2010).”
WikiReading: A Novel Large-scale Language Understanding Task over Wikipedia (2016, background): “Extensions to distant supervision include explicitly modelling whether the relation is actually expressed in the sentence (Riedel et al., 2010), and jointly reasoning over larger sets of sentences and relations (Sur-deanu et al., 2012).”
Position-aware Attention and Supervised Data Improve Slot Filling (2017, background): “For example, Riedel et al. (2010) show that up to 31% of the distantly supervised labels are wrong when creating training data from aligning Freebase to newswire text.”
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…”
Connecting Language and Knowledge Bases with Embedding Models for Relation Extraction (2013, methodology): “(2009) used Freebase to train weakly supervised relational extractors on Wikipedia, an approach generalized by the multiinstance learning frameworks (Riedel et al., 2010; Hoffmann et al., 2011; Surdeanu et al., 2012).”
Continual Relation Learning via Episodic Memory Activation and Reconsolidation (2020, background): “…al., 2015; Xu et al., 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; Bal-dini Soares et al., 2019), focuses on…”
FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation (2018, methodology): “Following recent meta-learning tasks ( Vinyals et (Riedel et al., 2010).”
Data Programming: Creating Large Training Sets, Quickly (2016, background): “Basic extensions group these mapped examples by the particular textual pattern w that they occur with, and cast the problem as a multiple instance learning one [13, 23].”
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 .”
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.”
Long-tail Relation Extraction via Knowledge Graph Embeddings and Graph Convolution Networks (2019, methodology): “We evaluate our models on the NYT dataset developed by (Riedel et al., 2010), which has been widely used in recent studies (Lin et al., 2016; Liu et al., 2017; Wu et al., 2017; Feng et al., 2018).”
Distant Supervision Relation Extraction with Intra-Bag and Inter-Bag Attentions (2019, methodology): “Thus, the relation extraction method proposed in (Riedel et al., 2010) argued that the distant supervision assumption was too strong and relaxed it to expressed-at-least-once assumption.”
GraphRel: Modeling Text as Relational Graphs for Joint Entity and Relation Extraction (2019, methodology): “We use the NYT (Riedel et al., 2010) and WebNLG (Gardent et al.”