Extracting Patterns and Relations from the World Wide Web

By Sergey Brin. 1998

Subfields: Information Retrieval & Search, Natural Language Processing

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Citations in the corpus

Toolformer: Language Models Can Teach Themselves to Use Tools (2023, background): “…to 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…”
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)”
Augmented Language Models: a Survey (2023, methodology): “As an alternative to standard fine-tuning, several authors propose to use bootstrapping techniques (e.g. Yarowsky, 1995; Brin, 1999) to leverage some form of indirect supervision.”
Pretrained Transformers for Text Ranking: BERT and Beyond (2020, background): “rch with a pattern, identify tuples, ﬁnd texts with those tuples, and from those learn more patterns, going around and around.14 Proposals along these lines date back to the late 1990s [Riloff, 1996, Brin, 1998, Agichtein and Gravano, 2000].15 Obviously, training data and extracted tuples gathered i”
It’s Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners (2020, methodology): “, Scudder, 1965; Yarowsky, 1995; Brin, 1999; McClosky et al., 2006) to easily combine several reformulations. Our modified version of PET uses masked language models (Devlin et al., 2019) to assign probabilities to sequences of text; this is similar to using them in a generative fashion (Wang and Ch”
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”
Neural Snowball for Few-Shot Relation Learning (2019, background): “Brin (1998) proposes to extract author-book facts with a small set of (author, book) pairs as input.”
Towards Understanding Gender Bias in Relation Extraction (2019, methodology): “…(Kambhatla, 2004; Zhou et al., 2005; Zhao and Grishman, 2005) and kernel-based methods (Lodhi et al., 2002; Zelenko et al., 2003), or semi-supervised (Brin, 1998; Agichtein and Gravano, 2000; Etzioni et al., 2005; Pantel and Pennacchiotti, 2006), or purely unsupervised (Et-zioni et al., 2008).”
Discovering Relations among Named Entities from Large Corpora (2004, methodology): “Brin proposed the bootstrapping method for relation discovery (Brin, 1998).”
Preemptive Information Extraction using Unrestricted Relation Discovery (2006, background): “Several existing works have tried to extract a certain type of relation by manually choosing different pairs of entities (Brin, 1998; Ravichandran and Hovy, 2002).”
Elementary: Large-Scale Knowledge-Base Construction via Machine Learning and Statistical Inference (2012, methodology): “Distant supervision is an increasingly popular techique to use entity-level data to generate (silver-standard) annotations from mention-level data (Hearst, 1992; Brin, 1999; Mintz et al., 2009; Zhang et al., 2012).”
Semi-Supervised Bootstrapping of Relationship Extractors with Distributional Semantics (2015, methodology): “2 Bootstrapping Relationship Extractors Brin (1999) developed DIPRE, the ﬁrst system to apply bootstrapping for RE, which represents the occurrences of seeds as three contexts of strings: words before the ﬁrst entity (BEF), words between the two entities (BET), and words after the second entity (AFT”
Extracting structured data from Web pages (2003, background): “INTRODUCTION The World Wide Web is a vast and rapidly growing source of information.”
Context in Web Search. (2000, methodology): “Many methods have been proposed for such information extraction, see for example [2, 9, 20, 38, 39, 40, 58, 59].”
Snowball: extracting relations from large plain-text collections (2000, methodology): “After testing our implementation on the “author-title” task, which is to the best of our knowledge the only application of the DIPRE method reported in the literature[2], we had to make some modifications, motivated by the nature of our collections.”
Searching the Web (2001, background): “(10)Interestingly, mutually reinforcing relationships have been identified and exploited for other Web tasks, see for instance [9].”
Learning Dual Retrieval Module for Semi-supervised Relation Extraction (2019, background): “This motivates the study of semi-supervised relation extraction meth-ods [1, 3, 31], which seek to leverage a limited number of labeled sentences and a large unlabeled (background) corpus for model training.”