Learning Dictionaries for Information Extraction by Multi-Level Bootstrapping

By Ellen Riloff, Rosie Jones. 1999

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Citations in the corpus

Learning How to Ask: Querying LMs with Mixtures of Soft Prompts (2021, background): “” In general, there may be many varied lexical patterns that signal a particular relation, and having more patterns will get better coverage (Hearst, 1992; Riloff and Jones, 1999).”
Distant supervision for relation extraction without labeled data (2009, methodology): “A third approach has been to use a very small number of seed instances or patterns to do bootstrap learning (Brin, 1998; Riloff and Jones, 1999; Agichtein and Gravano, 2000; Ravichandran and Hovy, 2002; Etzioni et al., 2005; Pennacchiotti and Pantel, 2006; Bunescu and Mooney, 2007; Rozenfeld and Fel”
Continual Relation Learning via Episodic Memory Activation and Reconsolidation (2020, background): “…2011; Marcheggiani and Titov, 2016), relation clustering (Shinyama and Sekine, 2006; Elsahar et al., 2017; Wu et al., 2019), and data collection (Riloff et al., 1999; Et-zioni et al., 2005; Pantel and Pennacchiotti, 2006; Rozenfeld and Feldman, 2008; Nakashole et al., 2011; Zhu et al., 2009;…”
Learning Extraction Patterns for Subjective Expressions (2003, methodology): “Unanno-tated texts are easy to come by, so even if the classiﬁers can label only 30% of the sentences as subjective or ob-jective, they will still produce a large collection of labeled sentences.”
Measuring the Evolution of a Scientific Field through Citation Frames (2018, background): “To avoid semantic drift (Riloff and Jones, 1999), a bootstrapped pattern was only included as a feature if the majority of its occurrences were with a single citation function.”
Neural Snowball for Few-Shot Relation Learning (2019, neutral): “(Riloff et al., 1999; Etzioni et al., 2005; Pantel and Pennacchiotti, 2006; Rozenfeld and Feldman, 2008; Nakashole et al., 2011).”
Design Challenges and Misconceptions in Named Entity Recognition (2009, background): “Given these ﬁndings, several approaches have been proposed to automatically extract comprehensive gazetteers from the web and from large collections of unlabeled text (Etzioni et al., 2005; Riloff and Jones, 1999) with limited impact on NER.”
Bootstrapping Coreference Classifiers with Multiple Machine Learning Algorithms (2003, methodology): “In addition, we investigate a method for ranking unlabeled instances to be fed back into the bootstrapping loop as labeled data, aiming to alleviate the problem of performance deterioration that is commonly observed in the course of bootstrapping.”
Measuring Corporate Culture Using Machine Learning (2020, methodology): “Such a procedure, known as bootstrapping, is common in information retrieval literature for learning new semantic lexicons (Riloff and Jones 1999).”
Semi-Supervised Semantic Role Labeling (2009, background): “Several researchers have used semi-supervised techniques for language learning (Nigam et al. 2000; Blum & Mitchell 1998; Joachims 1999; Riloff & Jones 1999 ), but we believe that this area is not yet well explored and definitely not well understood.”
From distributional to semantic similarity (2004, methodology): “Another approach, often used for common and proper nouns, uses bootstrapping (Riloff and Shepherd, 1997) and multi-level bootstrapping (Riloff and Jones, 1999) to find a set of terms related to an initial seed set.”
A High-Performance Semi-Supervised Learning Method for Text Chunking (2005, methodology): “A number of bootstrapping methods have been proposed for NLP tasks (e.g. Yarowsky (1995), Collins and Singer (1999), Riloff and Jones (1999)).”
Web-Scale Distributional Similarity and Entity Set Expansion (2009, methodology): “Finally, semi-supervised methods have shown great promise for identifying and labeling entities (Riloff and Shepherd 1997; Riloff and Jones 1999; Banko et al. 2007; Downey et al. 2007; Pa ş ca et al. 2006; Pa ş ca 2007a; Pa ş ca 2007b; Pa ş ca and Durme 2008).”
Identifying Collocations for Recognizing Opinions (2001, methodology): “More work needs to be done investigating the actual collocation instances in each of these sets, but a surface scan of the resulting collocations suggests that the fixed-n-grams which we are finding are those composed of more frequent words, while the ugen-n-grams seem to be collocations which are m”
A Bootstrapping Method for Learning Semantic Lexicons using Extraction Pattern Contexts (2002, methodology): “Three semantic lexicon learners have previously been evaluated on the MUC-4 corpus (Riloff and Shepherd, 1997; Roark and Charniak, 1998; Riloff and Jones, 1999), and of these meta-bootstrapping achieved the best results.”
Coupled semi-supervised learning for information extraction (2010, background): “Because of this, many researchers have explored semi-supervised learning methods that use only a small number of labeled examples of the predicate to be extracted, along with a large volume of unlabeled text [5, 19, 1].”