Pattern Learning for Relation Extraction with a Hierarchical Topic Model

By Enrique Alfonseca, Katja Filippova, Jean-Yves Delort, Guillermo Garrido. 2012

84 citations recorded by the source corpus.

Subfields: Information Retrieval & Search, Natural Language Processing

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Citations in the corpus

Snorkel: Rapid Training Data Creation with Weak Supervision (2017, methodology): “• Distant supervision: Distant supervision generates training labels by heuristically aligning data points with an external knowledge base, and is one of the most popular forms of weak supervision [4, 22,32].”
Training Classifiers with Natural Language Explanations (2018, methodology): “ear the gold LF in the space of logical forms are often just as accurate (and sometimes even more accurate). Third, techniques for combining weak supervision sources are built to tolerate some noise (Alfonseca et al., 2012; Takamatsu et al., 2012; Ratner et al., 2018). The signiﬁcance of this is tha”
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].”
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 Extraction Using Distant Supervision (2018, background): “Pattern learning approaches ([3], [55], [71]) aim to better capture how relations are expressed in the text.”
An Unsupervised Text Mining Method for Relation Extraction from Biomedical Literature (2014, background): “The Open Information Extraction systems [13–15] do not need any manual data or rules, but the relational facts they extract are not disambiguated to entities and relations [16].”
The Role of Massively Multi-Task and Weak Supervision in Software 2.0 (2019, methodology): “With respect to weak supervision, we build on the paradigm introduced in the Snorkel system [27, 29], and extended thereafter [3, 12, 32, 40, 41]; for a more comprehensive review of prior and related weak supervision work, such as distant supervision [1, 15, 23] and crowdsourcing [4, 8, 9, 18, 45], ”
Combining Generative and Discriminative Model Scores for Distant Supervision (2013, methodology): “We use a feature-based extension (Roth and Klakow, 2013) of Alfonseca et al. (2012) to include bigrams for a more ﬁne-grained representation of the patterns.”
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].”
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].”
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].”
Zero-Shot Task Transfer (2019, background): “ethods such as [32][38] proposed generative models that use a ﬁxed number of user-deﬁned weak supervision to programatically generate synthetic labels for data in near-constant time. Alfonseca et al. [1] use heuristics for weak supervision to acccomplish hierachical topic modeling. Broadly, such wea”
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].”