Snowball: extracting relations from large plain-text collections

By Eugene Agichtein, Luis Gravano. 2000

1,482 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Management Science and Operations Research, Natural Language Processing

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Citations in the corpus

Toolformer: Language Models Can Teach Themselves to Use Tools (2023, background): “…improve models has been investigated in various contexts, ranging from word sense disambiguation (Yarowsky, 1995), relation extraction (Brin, 1999; Agichtein and Gravano, 2000), parsing (McClosky et al., 2006), sequence generation (He et al., 2020), few-shot text classification (Schick and…”
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)”
How Can We Know What Language Models Know? (2019, methodology): “Our first method is inspired by templatebased relation extraction methods (Agichtein and Gravano, 2000; Ravichandran and Hovy, 2002), which are based on the observation that words in the vicinity of the subject x and object y in a large corpus often describe the relation r.”
Distant supervision for relation extraction without labeled data (2009, methodology): “research in bootstrapping or unsupervised IE has used only simple lexical features, thereby avoiding the computational expense of parsing (Brin, 1998; Agichtein and Gravano, 2000; Etzioni et al., 2005), and the few systems that have used unsupervised IE have not compared the performance”
Improving Information Extraction by Acquiring External Evidence with Reinforcement Learning (2016, methodology): “Open Information Extraction Existing work in open IE has used external sources from the web to improve extraction accuracy and coverage (Agichtein and Gravano, 2000; Etzioni et al., 2011; Fader et al., 2011; Wu and Weld, 2010).”
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): “We evaluate five model architectures: BREDS (Batista, Martins, and Silva 2015) is an advanced version of the original snowball (Agichtein and Gravano 2000), which uses word embeddings for pattern selection; Fine-tuning stands for directly using Algorithm 1 with few-shot instances to train the new cl”
Towards Understanding Gender Bias in Relation Extraction (2019, methodology): “, 2003), or semi-supervised (Brin, 1998; Agichtein and Gravano, 2000; Etzioni et al., 2005; Pantel and Pennacchiotti, 2006), or purely unsupervised (Etzioni et al.”
Improving Information Extraction by Acquiring External Evidence with Reinforcement Learning by Adam Yala (2017, methodology): “Existing work in open IE has used external sources from the web to improve extraction accuracy and coverage [1, 7, 8, 21].”
Quantifying Similarity between Relations with Fact Distribution (2019, methodology): “Hence, there are accordingly various methods proposed for discovering more relations and their facts, including open information extraction (Brin, 1998; Agichtein and Gravano, 2000; Ravichandran and Hovy, 2002; Banko et al., 2007; Zhu et al., 2009; Etzioni et al., 2011; Saha et al., 2017) and relati”
Semi-automatic construction of a named entity dictionary for entity-based sentiment analysis in social media (2017, methodology): “These methods mainly use linguistic patterns based on statistical information [1,15,17].”
Learning from Explanations with Neural Execution Tree (2019, neutral): “Traditional systems use handcrafted rules (Hearst, 1992) or automatically learned rules (Agichtein & Gravano, 2000; Batista et al., 2015) to take a rule-based approach.”
Multimodal Learning for Web Information Extraction (2017, background): “[1] demonstrated an information extraction system called Snowball to extract relational tuples from newspaper documents.”
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.”
YAGO: A Large Ontology from Wikipedia and WordNet (2008, methodology): “These approaches use information extraction technologies that include pattern matching, natural-language parsing, and statistical learning [49,25,11,1,46,41,18].”
Using Corpus Statistics on Entities to Improve Semi-supervised Relation Extraction from the Web (2007, methodology): “The IE systems most similar to SRES are based on bootstrap learning: Mutual Bootstrapping (Riloff and Jones 1999), the DIPRE system (Brin 1998), and the Snowball system (Agichtein and Gravano 2000).”
TEGRA: Table Extraction by Global Record Alignment (2015, methodology): “This is in contrast to techniques such as wrapper induction [4, 12, 17], that utilize training examples to extract targeted relations with a specific schema.”