TextRunner: Open Information Extraction on the Web

By Alexander Yates, Michele Banko, Matthew Broadhead, Michael J. Cafarella, Oren Etzioni, Stephen Soderland. 2007

424 citations recorded by the source corpus.

Traditional information extraction systems have focused on satisfying precise, narrow, pre-specified requests from small, homogeneous corpora. In contrast, the TextRunner system demonstrates a new kind of information extraction, called Open Information Extraction (OIE), in which the system makes a single, data-driven pass over the entire corpus and extracts a large set of relational tuples, without requiring any human input. (Banko et al., 2007) TextRunner is a fully-implemented, highly scalable example of OIE. TextRunner's extractions are indexed, allowing a fast query mechanism.

Subfields: Artificial Intelligence, Information Systems

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Citations in the corpus

NaturalLI: Natural Logic Inference for Common Sense Reasoning (2014, background): “For instance, OpenIE systems (Yates et al., 2007; Fader et al., 2011) extract concise facts via surface or dependency patterns.”
Using Structured Events to Predict Stock Price Movement: An Empirical Investigation (2014, methodology): “In contrast, we extract structured events by leveraging Open Information Extraction technology (Open IE; Yates et al. (2007); Etzioni et al. (2011); Faber et al. (2011)) without predeﬁned event types, which can effectively solve the two problems above.”
Lifelong Event Detection with Knowledge Transfer (2021, methodology): “Ontology-based supervised IE methods such as (Lin et al., 2020) can produce more accurate and structured results with annotated training data than open-domain IE (Yates et al., 2007) while being limited to the ontology.”
Annotated Gigaword (2012, background): “As such, direct comparison to their meth-ods are difﬁcult or impossible.”
A Stylometric Inquiry into Hyperpartisan and Fake News (2017, methodology): “For example, Etzioni et al. (2008) propose to use their well-known tool Text Runner (Yates et al., 2007) to extract and index factual knowledge from the web, and to use the same technology to extract factual statements from a given text in question, matching them against the indexed facts to…”
Reasoning With Neural Tensor Networks for Knowledge Base Completion (2013, background): “The field of open information extraction [15], for instance, extracts relationships from millions of web pages.”
Core techniques of question answering systems over knowledge bases: a survey (2017, neutral): “Note that it is a relation expressed in natural language so the subject, object and predicate are not mapped to any KB. Tools that can extract this type of triples are for example ReVerb (Fader et al. 2011), TEXTRUNNER (Yates et al. 2007), WOE (Wu & Weld 2010) and PATTY (Nakashole et al. 2012).”
Leveraging Linguistic Structure For Open Domain Information Extraction (2015, methodology): “One line of work begins with Text-Runner (Yates et al., 2007) and ReVerb (Fader et al., 2011), which make use of computationally efﬁcient surface patterns over tokens.”
DeepDive: Declarative Knowledge Base Construction (2016, methodology): “Within this space, there are three styles of systems that based on classiﬁcation-based frameworks [2,3,6,12,45], maximum a posteriori (MAP) [21,29, 40], and probabilistic graphical models [10, 35, 49].”
Quantifying Similarity between Relations with Fact Distribution (2019, methodology): “As Open IE is significant for constructing KBs, many effective extractors have been proposed to extract triples, such as Text-Runner (Yates et al., 2007), ReVerb (Fader et al.”
Exploratory Learning (2013, background): “Another line of research considers the problem of “open information extraction”, in which no classes or seeds are used at all [28,12,9].”
Incremental Knowledge Base Construction Using DeepDive (2015, methodology): “ledge Base Construction (KBC) KBC has been an area of intense study over the last decade, moving from pattern matching [24] and rule-based systems [33,34] to systems that use machine learning for KBC [6{8, 10, 17, 18, 30, 38, 44, 51, 59, 63]. Many groups have studied how to improve the quality of sp”
Pattern Learning for Relation Extraction with a Hierarchical Topic Model (2012, methodology): “Two example systems implementing this paradigm are T EXT R UN - NER (Yates et al., 2007) and R E V ERB (Fader et al., 2011).”
Dynamic Memory Networks for Visual and Textual Question Answering (2016, background): “If the potential input is a large text corpus, QA becomes a combination of information retrieval and extraction (Yates et al., 2007).”
Random Walk Inference and Learning in A Large Scale Knowledge Base (2011, background): “Although there is a great deal of recent research on extracting knowledge from text (Agichtein and Gravano, 2000; Etzioni et al., 2005; Snow et al., 2006; Pantel and Pennacchiotti, 2006; Banko et al., 2007; Yates et al., 2007), much less progress has been made on the problem of drawing reliable infe”
Methods for exploring and mining tables on Wikipedia (2013, background): “Researchers have made signiﬁcant strides toward automatically extracting massive, open-domain knowledge bases from Web content [1–10].”
Knowledge-Driven Event Embedding for Stock Prediction (2016, background): “Ding et al. (2014) show that structured events from open information extraction (Yates et al., 2007; Fader et al., 2011) can achieve better performance compared to conventional features, as they can capture structured relations.”