Unsupervised named-entity extraction from the Web: An experimental study

By Oren Etzioni, Michael J. Cafarella, Doug Downey, Ana-Maria Popescu, Tal Shaked, Stephen Soderland, Daniel S. Weld, Alexander Yates. Artificial Intelligence, 2005

1,295 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

View Paper: https://www.semanticscholar.org/paper/421151fa75e40dd86414215abf29d9f2c052a2e1

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Citations in the corpus

Exploiting Wikipedia as External Knowledge for Named Entity Recognition (2007, methodology): “Many methods have been proposed for solving this problem by automatically extracting gazetteers from large amounts of texts (Riloff and Jones, 1999; Thelen and Riloff, 2002; Etzioni et al., 2005; Shinzato et al., 2006; Talukdar et al., 2006; Nadeau et al., 2006).”
Bridging CNNs, RNNs, and Weighted Finite-State Machines (2018, methodology): “Patterns (Hearst, 1992) are particularly useful tool in NLP (Lin et al., 2003; Etzioni et al., 2005; Schwartz et al., 2015).”
TextRunner: Open Information Extraction on the Web (2007, background): “(Etzioni et al., 2005) On a set of ten high-frequency relations, TextRunner found nearly as many correct extractions as KnowItAll (11,631 to 11,476), while reducing the error rate of KnowItAll by 33% (18% to 12%).”
BabelNet: The automatic construction, evaluation and application of a wide-coverage multilingual semantic network (2012, background): “State-of-the-art proposals such as KnowItAll [32], KnowNet [28], TextRunnner [9,30] and TaxoLearn [96] all aim at acquiring repositories of knowledge from the Web with minimal or no supervision.”
Open Information Extraction from the Web (2007, methodology): “Efﬁciency K NOW I T A LL [Etzioni et al. , 2005] is a state-of-the-art Web extraction system that addresses the automation challenge by learning to label its own training examples us-ing a small set of domain-independent extraction patterns.”
A Survey on Deep Learning for Named Entity Recognition (2018, methodology): “…plays an essential role in a variety of natural language processing (NLP) applications such as text understanding [2], [3], information retrieval [4], [5], automatic text summarization [6], question answering [7], machine translation [8], and knowledge base construction [9] etc. Evolution of NER.”
Web-Scale Distributional Similarity and Entity Set Expansion (2009, methodology): “Etzioni et al. (2005) and Pantel et al. (2004) show the advantages of using large quantities of generic Web text over smaller corpora for extracting relations and named entities.”
Relation Extraction Using Distant Supervision (2018, background): “Semi-supervised approaches ([1], [9], [17]), on the other hand, typically rely on bootstrap learning.”
Seed-Based Event Trigger Labeling: How far can event descriptions get us? (2015, background): “One such IE paradigm, including Pre-emptive IE (Shinyama and Sekine, 2006), On-demand IE (Sekine, 2006; Sekine and Oda, 2007) and Open IE (Etzioni et al., 2005; Banko et al., 2007; Banko et al., 2008), focuses on un-supervised relation and event discovery.”
Extracting Product Features and Opinions from Reviews (2005, methodology): “Given fact f and discriminator d, the computed PMI score is: PMI(f, d) = Hits(d+ f ) Hits(d)∗Hits(f ) The PMI scores are converted to binary features for a Naive Bayes Classifier, which outputs a probability associated with each fact (Etzioni et al., 2005).”
A Survey on Recent Advances in Named Entity Recognition from Deep Learning models (2018, methodology): “Nadeau et al. (2006) presented an unsupervised system for gazetteer building and named entity ambiguity resolution based on Etzioni et al. (2005) and Collins and Singer (1999) that combined an extracted gazetteer with commonly available gazetteers to achieve F-scores of 88%, 61%, and 59% on MUC-7…”
Improved Pattern Learning for Bootstrapped Entity Extraction (2014, methodology): “Another avenue for future work is to use our pattern scoring method for learning other kinds of rules, such as dependency patterns, and in different kinds of systems, such as hybrid entity learning systems (Etzioni et al., 2005; Carlson et al., 2010).”
Semantic annotation for knowledge management: Requirements and a survey of the state of the art (2006, methodology): “KnowItAll [43] automates extraction of large knowledge bases of facts from theWeb in a similar fashion toArmadillo.”
Continual Relation Learning via Episodic Memory Activation and Reconsolidation (2020, neutral): “, 2019), and data collection (Riloff et al., 1999; Etzioni et al., 2005; Pantel and Pennacchiotti, 2006; Rozenfeld and Feldman, 2008; Nakashole et al., 2011; Zhu et al., 2009; Gao et al., 2020).”
Unsupervised biomedical named entity recognition: Experiments with clinical and biological texts (2013, background): “It is noteworthy that previous works in this category focus only on entity classiﬁcation, which assume that the named entities have already been correctly extracted from the text.”
Espresso: Leveraging Generic Patterns for Automatically Harvesting Semantic Relations (2006, methodology): “…algorithms include (Riloff and Shepherd 1997), who used a semi-automatic method for discovering similar words using a few seed examples, KnowItAll (Etzioni et al. 2005) that performs large-scale extraction of facts from the Web, Mann (2002) who used part of speech patterns to extract a subset of…”