Semi-Supervised Bootstrapping of Relationship Extractors with Distributional Semantics

By David S. Batista, Bruno Martins, Mário J. Silva. 2015

60 citations recorded by the source corpus.

Semi-supervised bootstrapping techniques for relationship extraction from text iteratively expand a set of initial seed relationships while limiting the semantic drift. We research bootstrapping for relationship extraction using word embeddings to find similar relationships. Experimental results show that relying on word embeddings achieves a better performance on the task of extracting four types of relationships from a collection of newswire documents when compared with a baseline using TFIDF to find similar relationships.

Subfields: Artificial Intelligence, Natural Language Processing, Representation & Self-Supervised Learning

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Citations in the corpus

Exploiting Cloze-Questions for Few-Shot Text Classification and Natural Language Inference (2020, neutral): “The idea behind iPET – training multiple generations of models on data labeled by previous generations – bears resemblance to self-training and bootstrapping approaches for word sense disambiguation (Yarowsky, 1995), relation extraction (Brin, 1999; Agichtein and Gravano, 2000; Batista et al., 2015)”
Automatically Identifying Words That Can Serve as Labels for Few-Shot Text Classification (2020, methodology): “This is also closely related to approaches for extracting patterns in relation extraction (Brin, 1999; Agichtein and Gravano, 2000; Batista et al., 2015; Bouraoui et al., 2020).”
Neural Snowball for Few-Shot Relation Learning (2019, methodology): “(2) While ﬁne-tuning, distant supervision and Neural Snowball improve with the increase of seed numbers, BREDS and RelSN have little promotion.”
Learning to Annotate: Modularizing Data Augmentation for Text Classifiers with Natural Language Explanations (2019, background): “Traditional systems use handcrafted rules (Hearst, 1992) or automatically learned rules (Agichtein and Gravano, 2000; Batista et al., 2015) to take a rule-based approach.”
Discrete-State Variational Autoencoders for Joint Discovery and Factorization of Relations (2016, methodology): “Bootstrapping methods for relation extraction (Agichtein and Gravano, 2000; Brin, 1998; Batista et al., 2015) iteratively label new examples by finding the ones which are the most similar, according to some similarity function, to a seed set of labeled examples.”
Lightly-supervised Representation Learning with Global Interpretability (2018, methodology): “Batista et al. (2015) perform bootstrapping for relation extraction using pre-trained word embeddings.”
NERO: A Neural Rule Grounding Framework for Label-Efficient Relation Extraction (2020, methodology): “Traditional corpus-level RE systems take a rule-based approach, in which rules are either handcrafted [10] or automatically learned from large corpora [2, 4].”
Osprey: Weak Supervision of Imbalanced Extraction Problems without Code (2019, methodology): “[2] has taken a semi-supervised approach for relations-extraction using a bootstrapping method.”
Learning from Explanations with Neural Execution Tree (2019, unknown): “Traditional systems use handcrafted rules (Hearst, 1992) or automatically learned rules (Agichtein & Gravano, 2000; Batista et al., 2015) to take a rule-based approach.”