Automatically Generating Extraction Patterns from Untagged Text

By Ellen Riloff. 1996

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In Natural Language Processing

Citations in the corpus

Data Augmentation for BERT Fine-Tuning in Open-Domain Question Answering (2019, methodology): “The roots of the distant supervision techniques we use trace back to at least the 1990s (Yarowsky, 1995; Riloff, 1996), although the term had not yet been coined.”
Pretrained Transformers for Text Ranking: BERT and Beyond (2020, background): “eratively: search 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 tup”
Self-Training With Noisy Student Improves ImageNet Classification (2019, neutral): “, [71, 96, 68, 67]).”
Self-Training and Pre-Training are Complementary for Speech Recognition (2020, neutral): “This includes classical self-training [9, 10, 11] which demonstrated strong results [12, 13, 2, 14, 15] by pseudolabeling unannotated audio data and then retraining the final system with the additional labeled data.”
Open-World Instance Segmentation: Exploiting Pseudo Ground Truth From Learned Pairwise Affinity (2022, neutral): “Selfsupervised learning [10, 11, 20, 42] is the most well-known; self-learning [46, 47, 59, 62] is another approach, based on the classical idea of adding high-confidence guessed labels to previously unlabeled data, and then combining this “pseudo ground truth” data with real ground truth.”
Rethinking Pre-training and Self-training (2020, neutral): “, [8–10]).”
Meta Pseudo Labels (2020, methodology): “The methods of Pseudo Labels or self-training [57, 81, 55, 36] have been applied successfully to improve state-ofthe-art models in many computer vision tasks such as image classification (e.”
Learning Extraction Patterns for Subjective Expressions (2003, methodology): “AutoSlog-TS then ranks the extraction patterns using a metric called RlogF (Riloff, 1996) and asks a human to review the ranked list and make the ﬁnal decision about which patterns to keep.”
Snowball: extracting relations from large plain-text collections (2000, methodology): “For this, we adopt a metric originally proposed by Riloff [12] to evaluate extraction patterns generated by the Autoslog-TS information extraction system according to the formulaRlogF (p) = relevance rate(p)·log2(frequency(p)).”
Learning Dictionaries for Information Extraction by Multi-Level Bootstrapping (1999, methodology): “We used AutoSlog (Riloff 1993; 1996a) in an exhaustive fashion to generate extraction patterns for every noun phrase in the corpus.”
An Overview of Deep Semi-Supervised Learning (2020, methodology): “In self-training [173, 138, 131, 132], the small amount of labeled data D l is ﬁrst used to train a prediction function f θ .”
Learning surface text patterns for a Question Answering System (2002, unknown): “Similar techniques have been investigated extensively in the field of information extraction (Riloff, 96).”
Mining knowledge from text using information extraction (2005, background): “Developing semi-supervised learning methods for IE is a related research direction in which there has been only a limited amount of work [62].”
Seed-Based Event Trigger Labeling: How far can event descriptions get us? (2015, methodology): “Bootstrapping methods (such as (Yangarber et al., 2000; Agichtein and Gravano, 2000; Riloff, 1996; Greenwood and Stevenson, 2006; Liao and Grishman, 2010a; Stevenson and Greenwood, 2005; Huang and Riloff, 2012)) have been widely applied in IE.”
Mining the peanut gallery: opinion extraction and semantic classification of product reviews (2003, methodology): “One technique uses linguistic units called relevancy signatures as part of CIRCUS, a tool for sorting documents [19].”
Identifying Sources of Opinions with Conditional Random Fields and Extraction Patterns (2005, methodology): “…the examples below: S1: Taiwan-born voters favoring independence... 1In related work, we investigate methods to identify the opinion expressions (e.g., Riloff and Wiebe (2003), Wiebe and Riloff (2005), Wilson et al. (2005)) and the nesting structure of sources (e.g., Breck and Cardie (2004)).”
A Bootstrapping Method for Learning Semantic Lexicons using Extraction Pattern Contexts (2002, methodology): “Basilisk scores each pattern using the RlogF metric that has been used for extraction pattern learning (Riloff, 1996).”
Machine Learning for Information Extraction in Informal Domains (2000, background): “…in the IE community has shown, machine learning (ML) can be of service, both in performing this fragment-to-slot mapping (Kim & Moldovan, 1995; Riloff, 1996; Soderland, 1996), and in solving associated tasks (Aone & Benett, 1996; Cardie, 1993; McCarthy & Lehnert, 1995; Riloff & Lehnert, 1994;…”