Reducing Wrong Labels in Distant Supervision for Relation Extraction

By Shingo Takamatsu, Issei Sato, Hiroshi Nakagawa. 2012

217 citations recorded by the source corpus.

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Citations in the corpus

Training Classifiers with Natural Language Explanations (2018, methodology): “Various extensions have focused on aggregating a variety of supervision sources by learning generative models from noisy labels (Alfonseca et al., 2012; Takamatsu et al., 2012; Roth and Klakow, 2013; Ratner et al., 2016; Varma et al., 2017).”
Data Programming: Creating Large Training Sets, Quickly (2016, methodology): “Other extensions actually model the accuracy of this pattern w using a discriminative feature-based model [25], or generative models such as hierarchical topic models [1,24,29].”
Socratic Learning: Augmenting Generative Models to Incorporate Latent Subsets in Training Data (2016, methodology): “Recently, generative models have been used to combine various sources of weak supervision [1, 27, 23].”
Label Noise Reduction in Entity Typing by Heterogeneous Partial-Label Embedding (2016, background): “In the context of distant supervision, label noise issue has been studied for other information extraction tasks such as relation extraction [27] and slot ﬁlling [11].”
DSGAN: Generative Adversarial Training for Distant Supervision Relation Extraction (2018, background): “Takamatsu et al. (2012) build a noise-ﬁltering strategy based on the linguistic features extracted from many NLP tools, including NER and dependency tree, which inevitably suffers the error propagation problem; while we just utilize word embedding as the input information.”
Combining Generative and Discriminative Model Scores for Distant Supervision (2013, background): “…that separates relation-speciﬁc distributions from noise distributions by using hierarchical topic models; And the pattern correlation approach (Takamatsu et al., 2012) that assumes that contexts which match argument pairs have a high overlap in argument pairs with other patterns expressing…”
Snuba: Automating Weak Supervision to Label Training Data (2018, methodology): “Other methods, including our previous work [52, 40, 54], rely on generative models to learn accuracies and dependencies among labeling sources [2, 42, 50].”
Training Complex Models with Multi-Task Weak Supervision (2018, background): “Common weak supervision sources include external knowledge bases [26; 39; 8; 33], heuristic patterns [14; 29], feature annotations [25; 38], and noisy crowd labels [17; 11].”
Snorkel: rapid training data creation with weak supervision (2019, methodology): “Other methods use generative models with hand-specified dependency structures to label data for specific modalities, such as topic models for text [3] or denoising distant supervision sources [50,57].”
Relation Extraction Using Distant Supervision (2018, background): “The authors of Reference [62] propose to distinguish the patterns that express a relation from those that do not.”
Reinforcement Learning for Relation Classification From Noisy Data (2018, background): “There are other approaches to reduce the noise of distant supervision using active learning (Sterckx et al. 2014) and negative patterns (Takamatsu, Sato, and Nakagawa 2012).”
Inferring Generative Model Structure with Static Analysis (2017, background): “Recently, generative models have been used to create training labels from various weak supervision sources, such as heuristics or knowledge bases, by modeling the true class label as a latent variable [1, 2, 27, 31, 36, 37].”
Learning the Structure of Generative Models without Labeled Data (2017, methodology): “Others use hierarchical topic models to generate additional training data for weak supervision, but they do not support user-provided heuristics (Alfonseca et al., 2012; Takamatsu et al., 2012; Roth & Klakow, 2013a;b).”
Relation Classification via Convolutional Deep Neural Network (2014, background): “Furthermore, Takamatsu et al. (2012) noted that the relaxed DS assumption would fail and proposed a novel generative model to model the heuristic labeling process in order to reduce the wrong labels.”